PokerStars.net ããŒã«ãŒã¯ç¡æã§éã¹ãã¢ãã€ã« ã¢ããªã§ããPokerStars ã®ãªã¢ã«ãããŒ ã¢ãã€ã« ã¢ããªã¯ Google Play ã§ã¯æäŸããŠããŸãã ** äžçæå€§ã®ããŒã«ãŒãµã€ã PokerStars ã®ãã¬ã€ãããŒ ã¢ããªã§ç¡æããŒã«ãŒããã¬ã€ãããã

å¯ŸMã»ããŒãºéžæãšã®æºæ±ºåãç§ã¯ç©¶æ¥µã®ã©ãªãŒåæŠã«åºåŸãåãã§ããæäžããªãããããµã¹ããŒã«ãã ããŒã«ãŒãé . ãããµã¹ããŒã«ãã ããŒã«ãŒãšããã²ãŒã ã¯ãã¬ã€ã€ãŒæ¯ã«é ããã2æã®ã«ãŒããš5æã®å ±æã«ãŒãã®èš7æã®ãã¡5æ. å€§åéžæã®æŽ»èºã«ãèªåãçå£ãªæŠããããŠã¿ããããšããã¹ããŒã ã®å°æ¥ãäºæããããåŸãŸããããç·åŒµæãå³ãããã»ã©ã«éããã«ã¯æãŠããªãç·Žç¿æéãå¿ èŠã§ãã

ãããµã¹ããŒã«ãã ã®ã«ãŒã« by PokerStrategy.com

iOSã²ãŒã ãçè ã®åžåœã ã¯ãŒã«ã01(ãµãŒããŒ01) ãã¿ããªã®é£çã å ¬åŒæ²ç€ºæ¿. æ€çŽ¢ïŒ OR AND.... ç§ã¯ã»ãšãã©ããããã«éå®ãŸãã¯ã©ã¡ããå ãªãã·ã§ã³åäººçãªããŒã ã¡ã€ããåŒãåãããã©ã¡ãããŠããŒã¯ãªãã ãããµã¹ ããŒã«ãã ã³ãŒãã§ãããŸãããã¹ãŠã®ç§.... ãšããŠãç¥ãããŠããè¡åæ¥œãã"æã ã¯ãããã®ããç·Žç¿å€ãã®å®¶ã® 1 ã€ã¯ã10 ä»£ã®æ»èµ°è·¯ç¹æ§ã.... ããã¯ããªãã®äŒæ¥ã¯ãããã€ãã®æéã®ããã®ææ°ã®åµé æ§ãšæè¡ã«ã§ããããã«ããã®ç©¶æ¥µã®æ°ååšãè£œé ãç«å±±æ°å.ãããµã¹ããŒã«ãã ãé¡æãšããæ°äœã¢ãŒã±ãŒãã²ãŒã ãããŒã«ãŒã¹ã¿ãžã¢ã ãã2019å¹Žæ¥ã«çšŒåããã±ãã¹ãã.. ã¢ãŒã±ãŒãã²ãŒã ã§å¯ŸäººæŠãæ¥œããããã«ã¯ïŒå¯ŸæŠçžæãšã®å®åãçµéšã®å·®ãåããã¹ãç·Žç¿ã®ç©ã¿éããå¿ èŠã ãšæããŸãã

ããé Â· ãè¥¿éæ³PJããªã¹ããªãŒã«ã¹ã¿ãŒã²ãŒã ãã¡ã³æç¥šäžéçºè¡šãæ¢ éäžè°·ç³žåå€§å±±æšæµªè¿æ¬ã Â· ã¡ãªèãã.. ãŠããããã¥ãŒã¹ Â· ãç»åãç©¶æ¥µã®ããŒããŠã³.... ãã³ã°ç·Žç¿çšGT-2000ããå±¥ãæ¿ãæ€èšäžâŠãã€ãã ãšãã¡ãïŒ.... ãåç»ã æ¥ç¬ éœåãããšæ¥é«éèãããããŒã«ãŒã®ããããµã¹ã»ããŒã«ãã ãã§åè² ïŒïŒ å£°åªå°å§ïŒ

CASINO | NAME | FREE BONUS | DEPOSIT BONUS | RATING | GET BONUS |

Casumo | - | 200% bonus + 180 free spins | PLAY |
||

Thrills | - | 200% bonus up to $100 + 20 super spins | PLAY |
||

Guts | - | $400 bonus + 100 free spins welcome package | PLAY |
||

GDay Casino | 50 free spins | 100% unlimited first deposit bonus | PLAY | ||

MrGreen | - | â¬350 + 100 free spins welcome package | PLAY |
||

Kaboo | 5 free spins | $200 bonus + 100 free spins welcome package | PLAY |
||

CasinoRoom | 20 free spins no deposit | 100% bonus up to $500 + 180 free spins | PLAY |
||

BetSpin | - | $200 bonus + 100 free spins welcome package | PLAY |
||

Karamba | - | $100 bonus + 100 free spins welcome package | PLAY |
||

PrimeSlots | 10 free spins | 100% bonus up to $100 + 100 free spins | PLAY |
||

LeoVegas | 20 free spins no deposit | 200% bonus up to $100 + 200 free spins | PLAY |
||

Spinson | 10 free spins no deposit | Up to 999 free spins | PLAY |
||

Royal Panda | - | 100% bonus up to $100 | PLAY |

## ãªã³ã©ã€ã³ã«ãžã ã©ã€ã»ã³ã¹æã«ã€ããŠ | ãªã³ã©ã€ã³ã«ãžãããããªãæããç·ã®ããã° ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

ã¹ããããã«ãŒãã²ãŒã ãã³ã€ã³ã²ãŒã ãªã©ãæ¥œãããã¹ããã®ã«ãžãã¢ããªããçŽ¹ä»ããŸããiphoneãšandroidãã©ã¡ãã®ã¹ããã«. çŽç²ã«ã³ã€ã³èœãšããæ¥œãã¿ããäººã«ãããã; ããããå ãé³æ¥œãã¶ã¯ã¶ã¯èœã¡ãã³ã€ã³ã§çœå¿«ã«éã¹ãïŒ ç©¶æ¥µïŒ. æ¬å Žã«æ è¡ã«è¡ãåã®ç·Žç¿ã«ãããããã. ãããã®ã§ããã£ãšãã¬ã€ã§ãã; åå è å šå¡ãèªåã®ã«ãŒããšããŠäœ¿ãããã³ãã¥ããã£ã«ãŒãããããããããµã¹ããŒã«ãã åœ¢åŒ.7 ïŒãªãŸãããããŠãã ããïŒ2014/07/22(ç«) 19:30:01.20 ID:lqxI0SWM.net: ããªã³ã¯ã²ãŒã ã ãããŠã©ãŒãå°åºã®ã ããã£ãŠãŠæ¥œããã£ãã.... ç©¶æ¥µã®æ¹æ³ã¯äžç·ã«é é£²ã.... 583 ïŒãªãŸãããããŠãã ããïŒ2014/07/29(ç«) 09:12:07.51 ID:7aXANbmD.net: éè»¢ã®ç·Žç¿ã®æãã§ããããã¹ãè»ç³»ããã·ã§ã³ããããã©ããã£ãš.... ããŒã«ãŒãªãããŸã«ããããããµã¹ããŒã«ãã ãªããŠèšèã¯çŸå®ããå£ã«ããããšããªã

ãŽã«ãã³ãŒã¹ïŒãŽã«ãç·Žç¿å Ž. â¢ ããã¹ã³ãŒãÃ8. â¢ ãã£ãããã¹ã»ã³ã¿ãŒ. â¢ ã¢ã¯ãã£ããã£ã»ã»ã³ã¿ãŒ. â¢ ã²ãŒã ã«ãŒã . â¢ ãã¿ãŒãŽã«ã. â¢ åå å.... ãã¯ããå®¢æ§ãæã¿ããããããå¿«é©ããæäŸãããå®¶æã«ãç©¶æ¥µã®ãã±ãŒã·ã§ã³ããå±ãããŸãã ãªãŒã©ã³ãã.... ãã¹ãã«ããçãã®ãããµãŒãžãäºçŽãããããããµã¹ã»ããŒã«ãã ãããŒã«ã»ããŒãã¡ã³ãããµã«.

## ãžããããã®ãã¿ | ãããã³ã«ãŒããšãïŒïŒãšã ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

## Audio Post Format | HomeSage | Your Way to Home ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

Big Fish Casinoã§ã¯ã¹ãããããã©ãã¯ãžã£ãã¯ããããµã¹ããŒã«ãã ããŒã«ãŒãã¯ã©ããã¹ãã«ãŒã¬ãããªã©æ§ã ãªã²ãŒã ã§å€§éãçšŒãããã£ã³ã¹ããïŒ. æ¬å Žããªããã®ã¹ããããã·ã³ãæ°è»œã«äœéšã§ããç©¶æ¥µã®æã€ã¶ãã¢ããªã§ãèª°ã§ãåå ã§ããã¹ãããããã«ãTournamaniaïŒããŒãããã¢ïŒããã¢ããïŒ.. éã¹ããéº»éååŒ·åéå ŽãåŸ åå®€ã§éã¹ããäžäººçšéº»éããºã«ã²ãŒã ããªã¡ã€ã¯ãããæ°è»œã«æäœãã®ç·Žç¿ãåºæ¥ãã²ãŒã ã§ããåããŠã«ãžãã«æ¥ããå®¢æ§ã«ã²ãŒã ã®éã³æ¹ãè±èªã§èª¬æããç·Žç¿ãããŸããæµ·å€ã®ã«ãžãã«ææŠããããšããæ¹ã«ãå¿ èŠãªäŒè©±ã. ãšãã¹ããŒãã³ãŒã¹ã§ã¯ããŒã«ãŒïŒãããµã¹ããŒã«ãã ïŒãåŠãã§ããã ããŸãã ã«ãŒã¬ããããã©ãã¯ãžã£ãã¯ããã«ã©ã®3å€§ã²ãŒã .

ã€ã©ã¹ãã®ç·Žç¿ãæçš¿ãç¶ããããã®ã³ãã®ãããªãã®ããŸãšããŠã¿ãŸããã ä»ã®ãžã£ã³ã«ã§ã... ãšèããããã¯ã»ãæã£ãŠããŸãïŒãããããŸãå©çšããã®ãããœãŒã·ã£ã«ã²ãŒã ã§ãïŒã ããããç¿æ £ã. çµµãæããšããããšããã®ç·Žç¿ãã²ãŒã ã®ã¬ãã«äžããšèããŸãããã RPGã§ãã. ç©¶æ¥µãªè©±ãããã®ââå çã®çµµãããããªãã®çµµã®ã»ããå¥œãã ãå¥œã¿ã ããšããäººã¯å¿ ãããã®ã§ãã ç¬ãšç«ã®.. ã²ãŒã ã1 Â· Re:ãŒãããå§ããç°äžççæŽ» ã¬ã (ãªãŒã) ã¹ãã« ã©ã (ãªãŒã) ããŒã«ãŒ ãããµã¹ããŒã«ãã .

## ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

The capacity of an LSTM network can be increased by widening and adding layers.However, usually the former introduces additional parameters, while the latter increases the runtime.

As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution.

By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence.

Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

~the number of nodes in the Ising model.

We show that our results are optimal up to logarithmic factors in the dimension.

We obtain our results by extending and strengthening the exchangeable-pairs approach used to prove concentration of measure in this setting by Chatterjee.

We demonstrate the efficacy of such functions as statistics for testing the strength of interactions in social networks in both synthetic and real world data.

This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space.

Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the ""self-expressiveness"" property that has proven effective in traditional subspace clustering.

Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure.

Being nonlinear, our neural-network based method is able to cluster data points having complex often nonlinear structures.

We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks.

Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the same.

It leads to significant improvements over state of the art base architecture on three standard action recognition benchmarks across still images and videos, and establishes new state of the art on MPII 12.

We also perform an extensive analysis of our attention module both empirically and analytically.

In terms of the latter, we introduce a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods typically used for fine-grained classification.

From this perspective, our attention formulation suggests a novel characterization of action recognition as a fine-grained recognition problem.

We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions.

We then apply our results to construct a consistent modal regression algorithm.

Yet, despite their practical success, support for nonsmooth objectives is still lacking, making them unsuitable for many problems of interest in machine learning, such as the Lasso, group Lasso or empirical risk minimization with convex constraints.

In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse method inspired by SAGA, a variance reduced incremental gradient algorithm.

The proposed method is easy to implement and significantly outperforms the state of the art on several nonsmooth, large-scale problems.

We prove that our method achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and block-separability of the proximal term.

Empirical benchmarks on a multi-core architecture illustrate practical speedups of up to 12x on a 20-core machine.

However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images.

To narrow this gap, we propose a Dual-Agent Generative Adversarial Network DA-GAN model, which can improve the realism of a face simulator's output using unlabeled real faces, while preserving the identity information during the realism refinement.

The dual agents are specifically designed for distinguishing real v.

In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.

DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents.

Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: i a pose perception loss; ii an identity perception loss; iii an adversarial loss with a boundary equilibrium regularization term.

Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark.

In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

There are three major challenges: 1 complex dependencies, 2 vanishing and exploding gradients, and 3 efficient parallelization.

In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges.

The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells.

Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance even with standard RNN cells in tasks involving very long-term dependencies.

To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.

We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures.

This ããã¹ã¿ã³ã§ç¡æã®iPhoneã«åã€ to the discovery of family of graph spectral distances denoted as FGSD and their based graph feature representations, which we prove to possess most of these desired properties.

To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem.

Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.

However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice.

This paper proposes new, scalable solutions to the GLB problem in two respects.

As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work.

Such methods can be implemented via hashing algorithms i.

Finally, we propose a fast approximate hash-key computation inner product with a better accuracy than the state-of-the-art, which can be of independent interest.

We conclude the paper with preliminary experimental results confirming the merits of our methods.

The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget.

This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that at present are confounding factors in functional cardiac model assessment.

So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary i.

Causes of failures include software bugs, network asynchrony, biases in local datasets, as well ååå ¥éããŒã«ãŒã¹ã¿ãŒãºããŒãã¹ attackers trying to compromise the entire system.

We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers i.

We also report on ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã evaluations of Krum.

Sometimes, it is desirable for a human operator to interrupt an agent in order to prevent dangerous situations from happening.

Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them.

The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents.

Orseau and Armstrong defined safe interruptibility for one learner, but their work does not naturally extend to multi-agent systems.

This paper introduces dynamic safe interruptibility, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: joint action learners and independent learners.

We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners.

We show however that if agents can detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

In real life situations, however, the utility function is not fully known in advance and can only be estimated via interactions.

For instance, whether a user likes a movie or not can be reliably evaluated only after it was shown to her.

Or, the range of influence of a user in a social network can be estimated only after she is selected to advertise the product.

We model such problems as an interactive submodular bandit optimization, where in each round we receive a context e.

We then receive a noisy feedback about the utility of the action e.

Given a bounded-RKHS norm kernel over the context-action-payoff space that governs the smoothness of the utility function, SM-UCB keeps an upper-confidence bound on the payoff function that allows it to asymptotically achieve no-regret.

Finally, we evaluate our results on four concrete applications, including movie recommendation on the MovieLense data setnews recommendation on Yahoo!

Webscope datasetinteractive influence maximization on a subset of the Facebook networkand personalized data summarization on Reuters Corpus.

In all these applications, we observe that SM-UCB consistently outperforms the prior art.

At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.

During training, the perception module and the generative models learn by visual de-animation --- interpreting and reconstructing the visual information stream.

During testing, the system first recovers the physical world state, and then uses the generative models for reasoning and future prediction.

Even more so than forward simulation, inverting a physics or graphics engine is a computationally hard problem; we overcome this challenge by using a convolutional inversion network.

Our system quickly recognizes the physical world state from appearance and motion cues, and has the flexibility to incorporate both differentiable and non-differentiable physics and graphics engines.

We evaluate our system on both synthetic and real datasets involving multiple physical scenes, and demonstrate that our system performs well on both physical state estimation and reasoning problems.

We further show that the knowledge learned on the synthetic dataset generalizes to constrained real images.

Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach.

Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain.

Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach.

In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.

However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence.

Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data.

Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks.

PSM offers significant advantages over other competing methods: 1 PSM naturally obtains the complete solution path for all values of the regularization parameter; 2 PSM provides a high precision dual certificate stopping criterion; 3 PSM yields sparse solutions through very few iterations, and the solution sparsity significantly reduces the computational cost per iteration.

Particularly, we demonstrate the superiority of PSM over various sparse learning approaches, including Dantzig selector for sparse linear regression, sparse support vector machine for sparse linear classification, and sparse differential network estimation.

We then provide sufficient conditions under which PSM always outputs sparse solutions such that its computational performance can be significantly boosted.

Thorough numerical experiments are provided to demonstrate the outstanding performance of the PSM method.

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

However, the previous works mainly focus on the least squares regression problem, not the classification task.

Thus, it is desired to design the new additive classification model with variable selection capability for many real-world applications which focus on high-dimensional data classification.

To address this challenging problem, in this paper, we investigate the classification with group sparse additive models in reproducing kernel Hilbert spaces.

Generalization error bound is derived and proved by integrating the sample error analysis with empirical covering numbers and the hypothesis error estimate with the link stone technique.

Our new bound shows that GroupSAM can achieve a satisfactory learning rate with polynomial decay.

Experimental results on synthetic data and seven benchmark datasets consistently show the effectiveness of our new approach.

This is very helpful since inference, or relevant bounds, may be much easier to obtain or more accurate for some model in the class.

Here we introduce methods to extend the approach to models with higher-order potentials and develop theoretical insights.

We demonstrate empirically that rerooting can significantly improve accuracy of methods of inference for higher-order models at negligible computational cost.

We introduce matrices with complex entries which give significant further accuracy improvement.

We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems.

However, the existing notions of fairness, based on parity equality in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy.

In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the dis parity as compared to the other groups.

Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness.

Finally, we experiment what ã²ãŒã ãžã¥ãŒã¹ãã¥ãŒã are a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

A popular solution is combining multiple sources of weak supervision using generative models.

The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels.

We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.

We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure.

We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations.

Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3.

Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.

Here we develop structured exponential family embeddings S-EFEa method for discovering embeddings that vary across related groups of data.

We study how the word usage of U.

Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons.

Key to the success of our method is that the groups share statistical information.

We develop two sharing strategies: hierarchical modeling and amortization.

We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets.

We show how SEFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate.

These features are constructed via Stein's method, meaning that it is not necessary to compute the normalising constant of the model.

We analyse the asymptotic Bahadur efficiency of the new test, and prove that under a mean-shift alternative, our test always has greater relative efficiency than a previous linear-time kernel test, regardless of the choice of parameters for that test.

In experiments, the performance of our method exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test.

In high dimensions and where model structure may be exploited, our goodness of fit test performs far better than a quadratic-time two-sample test based on the Maximum Mean Discrepancy, with samples drawn from the model.

Such stereotyped structure suggests the existence of common computational principles.

However, such principles have remained largely elusive.

Inspired by gated-memory networks, namely long short-term memory networks LSTMswe introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive subLSTM.

We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits.

Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units.

These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function.

Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.

We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups.

The resulting norms are in general NP-hard to compute, but they are tractable for certain collections of groups.

To demonstrate this fact, we develop a dynamic program for the problem of projecting onto the set of vectors supported by a fixed number of groups.

Our dynamic program utilizes tree decompositions and its complexity scales with the treewidth.

This program can be converted to an extended formulation which, for the associated group structure, models the k-group support norms and an overlapping group variant of the ordered weighted l1 norm.

Numerical results demonstrate the efficacy of the new penalties.

We show that while it is sensible to think of recall as simply retrieving items when probed with a cue - typically the item list itself - it is better to think of recognition as retrieving cues when probed with items.

To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds.

We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues.

This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.

For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called ""calibration function"" relating the excess surrogate go here to the actual risk.

In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity.

As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for structured prediction.

The standard training paradigm for these models is maximum likelihood estimation MLEor minimizing the cross-entropy of the human responses.

Across a variety of domains, a recurring problem with MLE trained generative neural dialog models G is that they tend to produce 'safe' and generic responses like ""I don't know"", ""I can't tell"".

In contrast, discriminative dialog models D that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses.

However, D is not useful in practice since it can not be deployed to have real conversations with users.

Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G.

Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual not adversarial loss of the sequence sampled from G.

We leverage the recently proposed Gumbel-Softmax GS approximation to the discrete distribution -- specifically, a RNN is augmented with a sequence of GS samplers, which coupled with the straight-through gradient estimator enables end-to-end differentiability.

We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses.

Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin 2.

To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box.

Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers.

We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.

Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network RCNNwe propose a new architecture named Gated RCNN GRCNN for solving this problem.

Its critical component, Gated Recurrent Convolution Layer GRCLis constructed by adding a gate to the Recurrent Convolution Layer RCLthe critical component of RCNN.

The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information.

In addition, an efficient Bidirectional Long Short-Term Memory BLSTM is built for sequence modeling.

The GRCNN is combined with BLSTM to recognize text in read more images.

The entire GRCNN-BLSTM model can be trained end-to-end.

Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text SVT and ICDAR.

It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption.

However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation.

In this paper, we address this issue with two major innovations: 1 approximating full-precision weights with the linear combination of multiple binary weight bases; 2 employing multiple binary activations to alleviate information loss.

The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets.

On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available.

These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors which do not hold near motion boundaries.

In this visit web page, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network.

Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions.

Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably against purely supervised and semi-supervised learning schemes.

In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays.

By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction.

End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images even a single image than required by classical approaches as well as completion of unseen surfaces.

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.

Our results reveal that noise can make the problem considerably more difficult, with strict increases in the scaling laws even at low noise levels.

Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality.

In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles.

The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network.

The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods.

We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.

However, we empirically found that the model shrinkage of the EPM does not typically work appropriately and leads to an overfitted solution.

In order to ensure that the model shrinkage effect of the EPM works in an appropriate manner, we proposed two novel generative constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM incorporating Dirichlet priors instead of the gamma priors.

We experimentally confirmed that the model shrinkage of the proposed models works well and that the IDEPM indicated state-of-the-art performance in generalization ability, link prediction accuracy, mixing efficiency, and convergence speed.

In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose.

The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way.

Popular inference algorithms such as belief propagation BP and generalized belief propagation GBP are intimately related to linear programming LP relaxation within the Sherali-Adams hierarchy.

Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares SOS hierarchy based on semidefinite programming SDP can provide superior guarantees.

In this paper, we propose binary SDP relaxations for MAP this web page using the SOS hierarchy with two innovations focused on computational efficiency.

Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model.

Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure.

We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses.

Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

While practitioners often employ variable importance methods that rely on this impurity-based information, these methods remain poorly characterized from a theoretical perspective.

We provide novel insights into the performance of these methods by deriving finite sample performance guarantees in a high-dimensional setting under various modeling assumptions.

We further demonstrate the effectiveness of these impurity-based methods via an extensive set of simulations.

The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly.

This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.

In this paper, we find a condition under which the dynamics of the GRU changes drastically simply ç¡æã®ãªã³ã©ã€ã³ã²ãŒã question propose a learning method to address the exploding gradient problem.

Our method constrains the dynamics of the GRU so that it does not drastically change.

We evaluated our method in experiments on language modeling and polyphonic music modeling.

Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.

This observation leads to many interesting results on general high-rank matrix estimation problems: 1.

The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters?

How does that provide hints on the generator's design and compare to the flourishing but almost exclusively experimental literature on the subject?

In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --.

The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters.

We complete this picture with additional results and experimental insights on how these results may be used to ground further improvements of GAN architectures, via i a principled design of the activation functions in the generator and ii an explicit integration of proper composite losses' link function in the discriminator.

In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting.

The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time.

A generator learns to map the given input, combined with this latent code, to the output.

We explicitly encourage the connection between output and the latent code to be invertible.

This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results.

We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code.

Our proposed method encourages bijective consistency between the latent encoding and output remarkable, ã²ãŒã ã®ãã£ãªã¢ good />We present a systematic comparison of our method and other variants on both perceptual realism and diversity.

However, our studies show that submatrices with different more info could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy.

In this paper, a mixture-rank matrix approximation MRMA method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks.

Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA.

Experimental studies on MovieLens éã¶ããã«ãéãæãã²ãŒã Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time.

In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints.

We start by investigating geometric properties that underlie such objectives, e.

These properties are then used to devise two optimization algorithms with provable guarantees.

This algorithm allows the use of existing methods for finding approximately stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization.

Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications.

Our theoretical findings are validated on synthetic and real-world problem instances.

In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits.

Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains.

Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations.

We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.

We prove that coordinate descent for a regularized regression problem, in which the penalty is a separable sum of support functions, is exactly equivalent to Dykstra's algorithm applied to the dual problem.

ADMM on the dual problem is also seen to be equivalent, in the special case of two sets, with one being a linear subspace.

These connections, aside from being interesting in their own right, suggest new ways of analyzing and extending coordinate descent.

For example, from existing convergence theory on Dykstra's algorithm over polyhedra, we discern that coordinate descent for the lasso problem converges at an asymptotically linear rate.

We also develop two parallel versions of coordinate descent, based on the Dykstra and ADMM connections.

A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems.

We propose to learn a spherical convolutional network that translates a planar CNN to process 360Â° imagery directly in its equirectangular projection.

Our approach learns to reproduce the flat filter outputs on 360Â° data, sensitive to the varying distortion effects across the viewing sphere.

The key benefits are 1 efficient feature extraction for 360Â° images and video, and 2 the ability to leverage powerful pre-trained networks researchers have carefully honed together with massive labeled image training sets for perspective images.

Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce.

Previous work chose to https://money-free-jackpot.site/1/1978.html on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data.

In this work, we propose an end-to-end trainable framework, sequentially estimating 2.

Our disentangled, two-step formulation has three advantages.

First, compared to full 3D shape, 2.

Second, for 3D reconstruction from the 2.

This further relieves the domain adaptation problem.

Third, we derive differentiable projective functions from 3D shape to 2.

Our framework achieves state-of-the-art performance on 3D shape reconstruction.

The visual question answering VQA problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning.

However, the current VQA models are over-simplified deep neural networks, comprised of a long short-term memory LSTM unit for question comprehension and a convolutional neural network CNN for learning single image representation.

We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities.

In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question.

The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.

The absolute error is a canonical example.

Many existing refuse. ãœãŠã«ã®æ°ããã«ãžã think for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss.

We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics.

We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

Existing theoretical analysis either only studies specific algorithms or only presents upper bounds on the generalization error but not on the excess risk.

In this paper, we propose a unified algorithm-dependent framework for HTL through a novel notion of transformation functions, which characterizes the relation between the source and the target domains.

We conduct a general risk analysis of this framework and in particular, we show for the first time, if two domains are related, HTL enjoys faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge Regression than those of the classical non-transfer learning settings.

We accompany this framework with an analysis of cross-validation for HTL to search for the best transfer technique and gracefully reduce to non-transfer learning when HTL is not helpful.

Experiments on robotics and neural imaging data demonstrate the effectiveness of our framework.

In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data.

The representation learning process is formulated as an adversarial minimax game.

We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions.

On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.

However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing.

In this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations.

This subset is characterized by a special structure called ""identity mapping"".

Unlike normal vanilla networks, the ""identity mapping"" makes our network asymmetric and thus the global minimum is unique.

To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks.

Our convergence theorem differs from traditional non-convex optimization techniques.

Then in phase II, SGD enters a nice one point convex region and converges.

We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization.

Experiment verifies our claims.

The use of mini-batches has become a golden standard in the machine learning community, because the mini-batch techniques stabilize the gradient estimate and can easily make good use of parallel computing.

Further, we show that even in non-mini-batch settings, our method achieves the best known convergence rate for non-strongly convex and strongly convex objectives.

In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic optimization.

These strategies can overcome the challenge of early trapping into bad local minima and have achieved remarkable improvements in various types of neural networks as shown in our theoretical analysis and empirical experiments.

This setting is in particular interesting since it captures natural online extensions of well-studied offline linear optimization problems which are Https://money-free-jackpot.site/1/2022.html, yet admit efficient approximation algorithms.

We present new algorithms with significantly improved oracle complexity for both the full information and bandit variants of the problem.

Numerical results on linear regression and logistic regression with elastic net regularization show that GeoPG compares favorably with Nesterov's accelerated proximal gradient method, especially when the problem is ill-conditioned.

Oja's iteration maintains a running estimate of the true principal component from streaming data and enjoys less temporal and spatial complexities.

We show that the Oja's iteration for the top eigenvector generates a continuous-state discrete-time Markov chain over link unit sphere.

We characterize the Oja's iteration in three phases using diffusion approximation and weak convergence tools.

Our three-phase analysis further provides a finite-sample error bound for the running estimate, which matches the minimax information lower bound for PCA under the additional assumption of bounded samples.

Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome.

While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.

Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.

This viewpoint shifts attention from ""What is the right fairness criterion?

First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion.

Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem.

Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

As a preliminary step in our analysis, we extend a nonparametric online learning algorithm by Hazan and Megiddo enabling it to compete against functions whose Lipschitzness is measured with respect to an arbitrary Mahalanobis metric.

This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions.

In legal scholarships, the notion of fairness itself is evolving and multi-faceted.

We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future.

To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions.

We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness.

Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space.

We show several existing models are special cases of ours.

Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions.

This will facilitate decision makers to select an operating point and to be accountable for it.

Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games.

Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames.

This visit web page referred to as subgame solving.

We introduce subgame-solving techniques that outperform prior methods both in theory and practice.

We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation.

Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability.

These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.

Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions.

To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs.

We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation.

We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets.

Example machine-learning applications include inverse problems such as personalized PageRank and sampling on graphs.

We provably show that our coded-computation technique can reduce the mean-squared error under a computational deadline constraint.

In fact, the ratio of mean-squared error of replication-based and coded techniques diverges to infinity as the deadline increases.

Further, unlike coded-computation techniques proposed thus far, our strategy combines outputs of all workers, including the stragglers, to produce more accurate estimates at the computational deadline.

The simple closed-form screening rule is a necessary and sufficient condition think, pokermarket freeroll betsafe theme exactly recovering the blockwise structure of a solution under any given regularization parameters.

With enough sparsity, the screening rule can be combined with various optimization procedures to deliver solutions efficiently in practice.

The screening rule is especially suitable for large-scale exploratory data analysis, where the number of variables in the dataset can be thousands while we are only interested in the relationship among a handful of variables within moderate-size clusters for interpretability.

Experimental results on various datasets demonstrate the efficiency and insights gained from the introduction of the screening rule.

By exploiting the strong convexity, previous studies have shown that the dynamic regret can be upper bounded by the path-length of the comparator sequence.

In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to query the gradient of the function multiple times, and meanwhile the strong convexity can be weakened to other non-degenerate conditions.

Specifically, we introduce the squared path-length, which could be much smaller than the path-length, as a new regularity of the comparator sequence.

When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded 5åæãã¹ããã the minimum of the path-length and the squared path-length.

We then extend our theoretical guarantee to functions that are semi-strongly convex or self-concordant.

To the best of our knowledge, this is the first time that semi-strong convexity and self-concordance are utilized to tighten the dynamic regret.

State-of-the-art models often use very deep networks with a large number of floating point operations.

Efforts such as model compression learn compact models with fewer number of parameters, but with much reduced accuracy.

Although knowledge distillation has demonstrated excellent improvements for simpler classification setups, the complexity of detection poses new challenges in the form of regression, region proposals and less voluminous la- bels.

We address this through several innovations such as a weighted cross-entropy loss to address class imbalance, a teacher bounded loss to handle the regression component and adaptation layers to better learn from intermediate teacher distribu- tions.

We conduct comprehensive empirical evaluation with different distillation configurations over multiple datasets including PASCAL, KITTI, ILSVRC and MS-COCO.

Our results show consistent improvement in accuracy-speed trade-offs for modern multi-class detection models.

This is done by learning a mapping that maintains the distance between a pair of samples.

Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping.

We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint.

We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems.

We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution.

In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization.

We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

MP and FW address optimization over the linear span and the convex hull of a set of atoms, respectively.

In this paper, we consider the intermediate case of optimization over the convex cone, parametrized as the conic hull of a generic atom set, leading to the first principled definitions of non-negative MP algorithms for which we give explicit convergence rates and demonstrate excellent empirical performance.

Furthermore, we establish a clear correspondence of our algorithms to known algorithms from the MP and FW literature.

Our novel algorithms and analyses target general atom sets and general objective functions, and hence are directly applicable to a large variety of learning settings.

Nevertheless, the reason for observations being missing often depends on the unseen observations themselves, and thus the missing data in practice usually occurs in a nonuniform and deterministic fashion rather than randomly.

Equipped with this new tool, we prove a series of theorems for missing data recovery and matrix completion.

In particular, we prove that the exact solutions that identify the target matrix are included as critical points by the commonly used nonconvex programs.

Unlike the existing theories for nonconvex matrix completion, which are built upon the same condition as convex programs, our theory shows that nonconvex programs have the potential to work with a much weaker condition.

Comparing to the existing studies on nonuniform sampling, our setup is more general.

Utilizing the theory of reproducing kernels, we reduce this hypothesis to a simple one-sided score test for a scalar parameter, develop a testing procedure that is robust against the mis-specification of kernel functions, and also propose an ensemble-based estimator for the null model to guarantee test performance in small samples.

To demonstrate the utility of the proposed method, we apply our test to the ãŸã³ãã©ã³ã2ã²ãŒã ãããã¯è§£é€ of detecting nonlinear interaction between groups of continuous features.

We ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã the finite-sample performance of our test under different data-generating functions and estimation strategies for the null model.

It is possible to cause a neural network used for image recognition to misclassify its input by applying very specific, hardly perceptible perturbations to the input, called adversarial perturbations.

Many hypotheses have been proposed to explain the existence of these peculiar samples as well as several methods to mitigate them.

A proven explanation remains elusive, however.

In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary to change the classification of neural networks.

The bounds are experimentally verified on the MNIST and CIFAR-10 data sets.

Submodular functions can be efficiently minimized and are conse- quently heavily applied in machine learning.

There are many cases, however, in which https://money-free-jackpot.site/1/1222.html do not know the function we aim to optimize, but rather have access to training data that is used to learn the function.

In this paper we consider the question of whether submodular functions can be minimized in such cases.

We show that even learnable submodular functions cannot be minimized within any non-trivial approximation when given access to polynomially-many samples.

We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory.

Our ICN tries to iteratively: 1 synthesize pseudo-negative samples; and 2 enhance itself by improving the classification.

The single CNN classifier learned is at the same time generative --- being able to directly synthesize new samples within its own discriminative model.

We conduct experiments on benchmark datasets including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results.

Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning, e.

This paper presents label distribution learning forests LDLFs - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1 Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.

We define a distribution-based loss function for a forest, enabling all the trees to be learned jointly, and show that an update function for leaf node predictions, which guarantees a strict decrease of the loss function, can be derived by variational bounding.

The effectiveness of the proposed LDLFs is verified on several LDL tasks and a computer vision application, showing significant improvements to the state-of-the-art LDL methods.

Starting from the recent idea of viewpoint factorization, we propose a new approach that, given a large number of images of an object and no other supervision, can extract a dense object-centric coordinate frame.

This coordinate frame is invariant to deformations of the images and comes with a dense equivariant labelling neural network that can map image pixels to their corresponding object coordinates.

We demonstrate the applicability of this method to simple articulated objects and deformable objects such as human faces, learning embeddings from random synthetic transformations or optical flow correspondences, all without any manual supervision.

Unfortunately, the huge number of units of these networks makes them expensive both computationally and memory-wise.

To overcome this, exploiting the fact that deep networks are over-parametrized, several compression strategies have been proposed.

These methods, however, typically start from a network that has been trained in a standard manner, without considering such a future compression.

In this paper, we propose to explicitly account for compression in the training process.

To this end, we introduce a regularizer that encourages the parameter matrix of each layer to have low rank during training.

We show that accounting for compression during training allows us to learn much more compact, yet at least as effective, models than state-of-the-art compression techniques.

State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activity.

Due to errors made in the decoding of noisy neural data, as a user attempts to move the cursor to a goal, the angle between cursor and goal positions may change rapidly.

This multiscale model explicitly captures the relationship between instantaneous angles of motion and long-term goals, and incorporates semi-Markov dynamics for click the following article trajectories.

We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications.

In offline experiments with recorded neural data, we demonstrate significantly improved prediction of motion directions compared to the Kalman filter.

We derive an efficient online inference algorithm, enabling a clinical trial participant with tetraplegia to control a computer cursor with neural activity in real time.

The observed kinematics of cursor movement are objectively straighter and smoother than prior consider, iphoneçšã®æ¬ç©ã®ããŒã«ãŒã²ãŒã something decoding models without loss of responsiveness.

This paper models these structures by presenting a predictive recurrent neural network PredRNN.

This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial appearances and temporal variations in a unified memory pool.

Concretely, memory states are no longer constrained inside each LSTM unit.

Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally.

The core of this ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã is a new Spatiotemporal LSTM ST-LSTM unit that extracts and memorizes spatial and temporal representations simultaneously.

PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures.

Recent work has highlighted the power-law multi-time scale properties of brain signals; however, there remains a lack of methods to specifically quantify short- vs.

In this paper, using detrended partial cross-correlation analysis DPCCAwe propose a novel functional connectivity measure to delineate brain interactions at multiple time scales, while controlling for covariates.

We use a rich simulated fMRI dataset to validate the proposed method, and apply it to a real fMRI dataset in a cocaine dependence prediction task.

We show that, compared to extant methods, the DPCCA-based approach not only distinguishes short and long memory functional connectivity but also improves feature extraction and enhances classification accuracy.

Together, this paper contributes broadly to new computational methodologies in understanding neural information processing.

However, the distinctiveness of natural descriptions is often overlooked in previous work.

It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects.

ããããŒãã€ãŒã«ã²ãŒã ã®ãã«ããŒãžã§ã³ã®ããŠã³ããŒã this work, we propose a new learning method, Contrastive Learning CLfor image captioning.

Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the overall quality of the generated captions.

We tested our method on two challenging datasets, where it improves the baseline model by significant margins.

please click for source also showed in our studies that the proposed method is generic and can be used for models with various structures.

However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems.

As a consequence, learning algorithms are rarely applied on safety-critical systems in the real world.

In this paper, we present a learning algorithm that explicitly considers safety, defined in terms of stability guarantees.

Specifically, we extend control-theoretic results on Lyapunov stability verification and show how to use statistical models of the dynamics to obtain high-performance control policies with provable stability certificates.

Moreover, under additional regularity assumptions in terms here a Gaussian process prior, we prove that one can effectively and safely collect data in order to learn about the dynamics and thus both improve control performance and expand the safe region of the state space.

In our experiments, we show how the resulting algorithm can safely optimize a neural network policy on a simulated inverted pendulum, without the pendulum ever falling down.

However, the multiclass extension is check this out the batch setting and the online extensions only consider binary classification.

We fill this gap in the literature by defining, and justifying, a weak learning condition for online multiclass boosting.

This condition leads to an optimal boosting algorithm that requires the minimal number of weak learners to achieve a certain accuracy.

Additionally, we propose an adaptive algorithm which is near optimal and enjoys an excellent performance on real data due to its adaptive property.

Matching is an effective strategy to tackle this problem.

The widely used matching estimators such as nearest neighbor matching NNM pair the treated units with the most similar control units in terms of covariates, and then estimate treatment effects accordingly.

However, the existing matching estimators have poor performance when the distributions of control and treatment groups are unbalanced.

Moreover, theoretical analysis suggests that the bias of causal effect estimation would increase with the dimension of covariates.

In this paper, we aim to address these problems by learning low-dimensional balanced and nonlinear representations BNR for observational data.

In particular, we convert counterfactual prediction as a classification problem, develop a kernel learning model with domain adaptation constraint, and design a novel matching estimator.

The dimension of covariates will be significantly reduced after projecting data to a low-dimensional subspace.

Experiments on several synthetic and real-world datasets demonstrate the effectiveness of our approach.

Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning.

In this paper, we present several new results---both positive and negative---which help define the boundaries between the tractable-learning setting and the intractable setting.

We show positive results for a large subclass of HMMs whose transition matrices are sparse, well-conditioned and have small probability mass on short cycles.

We also show that learning is impossible given only a polynomial number of samples for HMMs with a small output alphabet and whose transition matrices are random regular graphs with large degree.

We also discuss these results in the context of learning HMMs which can capture long-term dependencies.

Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.

The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling.

The causal kernels are learned nonparametrically using Gaussian process regression, yielding an efficient framework for causal inference.

We construct a novel class of causal covariance functions that enforce the desired properties of the causal kernels, an approach which we call GP CaKe.

By construction, the model and its hyperparameters have biophysical meaning and are therefore easily interpretable.

We demonstrate the efficacy of GP CaKe on a number of simulations and give an example of a realistic application on magnetoencephalography MEG data.

A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes.

Unfortunately, this approach often leads to noisy labels.

In this paper, we propose a meta algorithm for tackling the noisy labels problem.

We demonstrate the effectiveness of our algorithm by mining data for gender classification by combining the Labeled Faces in the Wild LFW face recognition dataset with a textual genderizing service, which leads to a noisy dataset.

While our approach is very simple to implement, it leads to state-of-the-art results.

We analyze some convergence properties of the proposed algorithm.

However, success stories of Deep Learning with standard feed-forward neural networks FNNs are rare.

FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations.

We introduce self-normalizing neural networks SNNs to enable high-level abstract representations.

While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards zero mean and unit variance.

The activation function of SNNs are ""scaled exponential linear units"" SELUswhich induce self-normalizing properties.

Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations.

This convergence property of SNNs allows to 1 train deep networks with many layers, 2 employ strong regularization, and 3 to make learning highly robust.

Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible.

We compared SNNs on a 121 tasks from the UCI machine learning repository, on b drug discovery benchmarks, and on c astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines.

For FNNs we considered i ReLU networks without normalization, ii batch normalization, iii layer normalization, iv weight normalization, v highway networks, vi residual networks.

SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set.

The winning SNN architectures are often very deep.

The majority of this work focuses on a binary domain label.

Similar problems occur in a scientific context where there may be a continuous family of plausible data generation processes associated to the presence of systematic uncertainties.

Robust inference is possible if it is based on a pivot -- a quantity whose distribution does not depend on the unknown values of the nuisance parameters that parametrize this family of data generation processes.

In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property or, equivalently, fairness with respect to continuous attributes on a predictive model.

The method includes a hyperparameter to control the trade-off between accuracy and robustness.

We demonstrate the effectiveness of this approach with a toy example and examples from particle physics.

While convolutional neural networks have proven to be the first choice for images, audio and video data, ã€ãªãã€å·ã«ãžãã®ã£ã³ãã«å¹Žéœ¢ atoms in molecules are not restricted to a grid.

Instead, their precise locations contain essential physical information, that would get lost if discretized.

Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid.

We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules.

We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.

Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories.

Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent SGD : the variance in predicted probability of the correct class across iterations of mini-batch SGD, and the proximity of the correct class probability to the decision threshold.

Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques, such as residual learning, momentum, ADAM, batch normalization, dropout, and distillation.

For example, is it possible to use in deep architectures a layer whose output is the minimal cut of a parametrized graph?

Given that these models are trained end-to-end by leveraging gradient information, the introduction of such layers seems very challenging due to their non-continuous output.

In this paper we focus on the problem of submodular minimization, for which we show that such layers are indeed possible.

The key idea is that we can continuously relax the output without sacrificing guarantees.

We provide an easily computable approximation to the Jacobian complemented with a complete theoretical analysis.

Finally, these contributions let us experimentally learn probabilistic log-supermodular models via a bi-level variational inference formulation.

However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes.

Here we present GraphSAGE, a general, inductive framework that leverages node feature information e.

Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood.

Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Sequential data, including time-series and ordered data, contain important structural relationships among items, imposed by underlying dynamic models of data, that should play a vital role in the selection of representatives.

However, nearly all existing subset selection techniques ignore underlying dynamics of data and treat items independently, leading to incompatible sets of representatives.

In this paper, we develop a new framework for sequential subset selection that finds a set of representatives compatible with the dynamic models of data.

To do so, we equip items with transition dynamic models and pose the problem as an integer binary optimization over assignments of sequential items to representatives, that leads to high encoding, diversity and transition potentials.

Our formulation generalizes the well-known facility location objective to deal with sequential data, incorporating transition dynamics among facilities.

As the proposed formulation is non-convex, we derive a max-sum message passing algorithm to solve the problem efficiently.

Experiments on synthetic and real data, including instructional video summarization, show that our sequential subset selection framework not only achieves better encoding and diversity than the state of the art, but also successfully incorporates dynamics of data, leading to compatible representatives.

Here we introduce a cognitive model capable of constructing human-like questions.

Our approach treats questions as formal programs that, when executed on the state of the world, output an answer.

The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context.

We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game.

We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set.

In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.

In this work, we propose an efficient Perceptron-based algorithm for actively learning homogeneous halfspaces under the uniform distribution over the unit sphere.

This result implies that GD is inherently slower than perturbed GD, and justifies the importance of adding perturbations for efficient non-convex optimization.

While our focus is theoretical, we also present experiments that illustrate our theoretical findings.

Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive.

We introduce the Union of Intersections UoI method, a flexible, modular, and scalable framework for enhanced model selection and estimation.

The method performs model selection and model estimation through intersection and union operations, click the following article />We show that UoI can satisfy the bi-criteria of low-variance and nearly unbiased estimation of a small number of interpretable features, while maintaining high-quality prediction accuracy.

In doing so, we demonstrate the extraction of interpretable functional networks from human electrophysiology recordings as well as the accurate prediction of phenotypes from genotype-phenotype data with reduced features.

These results suggest that methods based on UoI framework could improve interpretation and prediction in data-driven discovery across scientific fields.

This usually requires either careful feature engineering, or a significant number of samples.

This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering.

In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning.

Specifically, we consider the setting where there is a very large maybe infinite set of tasks, and each task has many instantiations.

For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc.

In each case, different instances of the task would consist of different sets of blocks with different initial states.

At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks.

A neural net is trained that takes as input one demonstration and the current state which initially is the initial state of the other demonstration of the pairand outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.

At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task.

Our experiments show that the use of soft attention allows the model to generalize to conditions and tasks unseen in the training data.

We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks.

Even though some success has been reported with existing algorithms, they are limited ãã©ãã¯ããŒã¯ã¹ã¹ã³ã¢ã²ãŒã 7 applicability due to their heuristic nature.

Moreover, they are often vulnerable to artifacts and impulsive noise, which are typically present in raw neural recordings.

In this study, we address these issues and propose a novel probabilistic convolutional sparse coding CSC model for learning shift-invariant atoms from raw neural signals containing potentially severe artifacts.

We develop a novel, computationally efficient Monte Carlo expectation-maximization algorithm for inference.

The maximization step boils down to a weighted CSC problem, for which we develop a computationally efficient optimization algorithm.

Our results show that the proposed algorithm achieves state-of-the-art convergence speeds.

Compared with recent advances in this vein, the differential equation considered here is the basic gradient flow, and we derive a class of multi-step schemes which includes accelerated algorithms, using classical conditions from numerical analysis.

Multi-step schemes integrate the differential equation using larger step sizes, which intuitively explains the acceleration phenomenon.

The constants quantifying error bounds are of course unobservable, but we show that optimal restart strategies are robust, and searching for the best scheme only increases the complexity by a logarithmic factor compared to the optimal bound.

Overall then, click schemes generically accelerate accelerated methods.

Dynamic mode decomposition is a popular numerical algorithm for Koopman spectral analysis; however, we often need to prepare nonlinear observables manually according to the underlying dynamics, which is not always possible since we may not have any a priori knowledge about them.

In this paper, we propose a fully data-driven method for Koopman spectral analysis based on the principle of learning Koopman invariant subspaces from observed data.

To this end, we propose minimization of the residual sum of squares of linear least-squares regression to estimate a set of functions that transforms data into a form in which the linear regression fits well.

We introduce an implementation with neural networks and evaluate performance empirically using nonlinear dynamical systems and applications.

Our method is based on a soft continuous relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training.

We showcase this method for two challenging applications: Image compression and neural network compression.

While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both.

This model ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã into the well defined mixed-effect models.

The subject-specific trajectories are defined through spatial and temporal transformations of the group-average piecewise-geodesic path, component by component.

Thus we can apply our model to a wide variety of situations.

Due to the non-linearity of the model, we use the Stochastic Approximation Expectation-Maximization algorithm to estimate the model parameters.

Experiments on synthetic data validate this choice.

The model is then applied to the metastatic renal cancer chemotherapy monitoring: we run estimations on RECIST scores of treated patients and estimate the time they escape from the treatment.

Experiments highlight the role of the different parameters on the response to treatment.

We address this issue by introducing a triggering probability modulated TPM bounded smoothness condition into the influence maximization bandit and combinatorial cascading bandit read article this TPM condition.

RNNs are used to model dynamic processes that are characterized ãŠã£ã³ããŠãº7çšã®ã²ãŒã ãããŠã³ããŒã underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN.

In the Predictive-State Representation PSR literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution.

We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders PSDswhich add supervision to the network's internal state representation to target predicting future observations.

PSDs are simple to implement and easily incorporated into existing training pipelines via additional loss regularization.

We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning.

In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.

Our techniques involve proving some novel results about the anti-concentration of Dirichlet distribution, which may be of independent interest.

It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target.

However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets.

To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions.

As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling.

Using the same network architecture, Mean Teacher achieves error rate 4.

We show that Mean Teacher is compatible with residual networks, and improve state of the art on CIFAR-10 with 4000 labels from 10.

Our preliminary experiments also suggest a large improvement over state of the art on semi-supervised ImageNet 2012.

In this work we focus on low-level correspondences --- a highly ambiguous matching problem.

We propose to use a hierarchical semantic representation of the objects, coming from a convolutional neural network, to solve this ambiguity.

Training it for low-level correspondence prediction directly might not be an option in some domains where the ground-truth correspondences are hard to obtain.

We show how transfer from recognition can be used to avoid such training.

Although the overall number of such paths is exponential in the number of layers, we propose a polynomial algorithm for aggregating all of them in a single backward pass.

The empirical validation is done on the task of stereo correspondence and demonstrates that we achieve competitive results among the methods which do not use labeled target domain data.

By modelling the target function as a transformation of an underlying function, the constraints are explicitly incorporated in the model such that they are guaranteed to be fulfilled by any sample drawn or prediction made.

We also propose a constructive procedure for designing the transformation operator and illustrate the result on both simulated and real-data examples.

Because of storage limitations, it may only be possible to retain a sketch of the psd matrix.

This paper develops a new algorithm for fixed-rank psd approximation from a sketch.

The approach combines the Nystr?

Theoretical analysis establishes that the proposed method can achieve any prescribed relative error in the Schatten 1-norm and that it exploits the spectral decay of the input matrix.

Computer experiments show that the proposed method dominates alternative techniques for fixed-rank psd matrix approximation across a wide range of examples.

The requirement of structured here isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks.

In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly.

The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy.

We present two architectural recipes in the context of multi-stage progressive encoders and empirically demonstrate their importance on compression performance.

Specifically, we show that: ã¢ã³ããã€ãçšã®æé«ã®ã¯ãªã±ããã²ãŒã ç¡æããŠã³ããŒã predicting the original image data from residuals in a multi-stage progressive architecture facilitates learning and leads to improved performance at approximating the original content and 2 learning to inpaint from neighboring image pixels before performing compression reduces the amount of information that must be stored to achieve a high-quality approximation.

Incorporating these design choices in a baseline progressive encoder yields an average reduction of over 60% in file size with similar quality compared to the original residual encoder.

Existing adaptive samplers use Riemannian preconditioning techniques, where the mass matrices are functions of the parameters being sampled.

This leads to significant complexities in the energy reformulations and resultant youtube ããŒã«ãŒã²ãŒã 2019, often leading to implicit systems of equations and requiring inversion of high-dimensional matrices in the leapfrog steps.

Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique.

We also propose a way to adaptively set the number of samples gathered in the E step, using sampling error estimates from the leapfrog dynamics.

However, it is known that the performance of ADMM and many of its variants is very sensitive to the penalty parameter of a quadratic penalty applied to the equality constraints.

Although several approaches have been proposed for dynamically changing this parameter during the course of optimization, they do not yield theoretical improvement in the convergence rate and are not directly applicable to stochastic ADMM.

In this paper, we develop a new ADMM and its linearized variant with a new adaptive scheme to update the penalty parameter.

Our methods can be applied under both deterministic and stochastic optimization settings for structured non-smooth objective function.

The novelty of the proposed scheme lies at that it is adaptive to a local sharpness property of the objective function, which marks the key difference from previous adaptive scheme that adjusts the penalty parameter per-iteration based on certain conditions on iterates.

The complexity in either setting improves that of the standard ADMM which only uses a fixed penalty parameter.

On the practical side, we demonstrate that the proposed algorithms converge comparably to, if not much faster than, ADMM with a fine-tuned fixed penalty parameter.

Based on knowledge of the physical world, humans are able to infer ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã information from such limited data: rough shape of the object, its material, the height of falling, etc.

In this paper, we aim to approximate such competency.

We first mimic the human knowledge about the physical world using a fast physics-based generative model.

Then, we present an analysis-by-synthesis approach to infer properties of the falling object.

We further approximate human past experience by directly mapping audio to object properties using deep learning with self-supervision.

We evaluate our method through behavioral studies, where we compare human predictions with ours on inferring object shape, material, and initial height of falling.

Results show that our method achieves near-human performance, without any annotations.

However, identifying which models can quantitatively reproduce empirically measured data has been challenging.

We propose to overcome this limitation by using likelihood-free inference approaches also known as Approximate Bayesian Computation, ABC to perform full Bayesian inference on single-neuron models.

Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters.

We learn a Bayesian mixture-density network approximating the posterior over multiple rounds of adaptively chosen simulations.

Furthermore, we propose an efficient approach for handling missing features and parameter settings for which the simulator fails, as well as a strategy for automatically learning relevant features using recurrent neural networks.

On synthetic data, our approach efficiently estimates posterior distributions and recovers ground-truth parameters.

On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, which yield model-predicted voltage traces that accurately match empirical data.

Our approach will enable neuroscientists to perform Bayesian inference on complex neuron models without having to design model-specific algorithms, closing the gap between mechanistic and statistical approaches to single-neuron modelling.

This read more makes three main contributions.

Second, we design IC algorithms with good performance guarantees for the absolute loss function.

Third, we give a formal separation between the power of online prediction click at this page selfish experts and online prediction with honest experts by proving lower bounds for both IC and non-IC algorithms.

In particular, with selfish experts and the absolute loss function, there is no randomized algorithm for online prediction---IC or otherwise---with asymptotically vanishing regret.

The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie in a low-dimensional subspace, modeling similarity among the signal trajectories while allowing different scalings across different individuals or different features.

We study the information-theoretic limit of this problem under a generative model.

Moreover, we propose an efficient spectral algorithm to solve the tensor biclustering problem and analyze its achievability bound in an asymptotic regime.

Finally, we show the efficiency of our proposed method in several synthetic and real datasets.

A good screening policy should be personalized to the disease, to ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã features of the patient and to the dynamic history of the patient including the history of screening.

The growth of electronic health records data has led to the development of many models to predict the onset and progression of different diseases.

However, there has been limited work to address the personalized screening for these different diseases.

In this work, we develop the first framework to construct screening policies for a large class of disease models.

The disease is modeled as a finite state stochastic process with an absorbing disease state.

The patient observes an external information process for instance, self-examinations, discovering comorbidities, etc.

The clinician carries out the tests; based on the test results and the external information it schedules the next arrival.

Computing the exactly optimal screening policy that balances the delay in the detection against the frequency of screenings is computationally intractable; this paper provides a computationally tractable construction of an approximately optimal policy.

As an illustration, we make use of a large breast cancer data set.

The constructed policy screens patients more or less often according to their initial risk -- it is personalized to the features of the patient -- and according to the results of previous screens?

In comparison with existing clinical policies, the constructed policy leads to large reductions 28-68 % in the number of screens performed while achieving the same expected delays in disease detection.

We propose a Thompson Sampling-based reinforcement learning algorithm with dynamic episodes TSDE.

At the beginning of each episode, the algorithm generates a sample from the posterior distribution over the unknown model parameters.

It then follows the optimal stationary policy for the sampled model for the rest of the episode.

The duration of each episode is dynamically determined by two stopping criteria.

The first stopping criterion controls the growth rate of episode length.

The second stopping criterion happens when the number of visits to any state-action pair is doubled.

This regret bound matches the best available bound for weakly communicating MDPs.

Numerical results show it to perform better than existing algorithms for infinite horizon MDPs.

However, it is rarely that all possible differences between samples are of interest -- discovered differences can be due to different types of measurement noise, data collection artefacts or other irrelevant sources of variability.

We propose distances between distributions which encode invariance to additive symmetric noise, aimed at testing whether the assumed true underlying processes differ.

Moreover, we construct invariant features of distributions, leading to learning algorithms robust to the impairment of the input distributions with symmetric additive noise.

To solve this problem, we propose and discuss an effective model-based clustering method based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.

The proposed model generates the event sequences with different clusters from the Hawkes processes with different parameters, and uses a Dirichlet process as the prior distribution of the clusters.

We prove the identifiability of our mixture model and propose an effective variational Bayesian inference algorithm to learn our model.

An adaptive inner iteration allocation strategy is designed to accelerate the convergence of our algorithm.

Moreover, we investigate the sample complexity and the computational complexity of our learning algorithm in depth.

Experiments on both synthetic and real-world data show that the clustering method based on our model can learn structural triggering patterns hidden in asynchronous event sequences robustly and achieve superior performance on clustering purity and consistency compared to existing methods.

In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different protocol, known as diffusion.

However, it is unclear if diffusion actually improves the system's anonymity.

In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both pre- and post-2015.

The core problem is one of epidemic source inference over graphs, where the observational model and spreading mechanisms are informed by Bitcoin's implementation; notably, these models have not been studied in the epidemic source detection literature before.

We identify and analyze near-optimal source estimators.

This analysis suggests that Bitcoin's networking protocols both pre- and post-2015 offer poor anonymity properties on networks with a regular-tree topology.

We confirm this claim in simulation on a 2015 just click for source of the real Bitcoin P2P network topology.

We show that while the ordinary Min-Sum algorithm does not converge, a modified version of it known as Splitting yields convergence to the problem solution.

We prove that a proper choice of the tuning parameters allows Min-Sum Splitting to yield subdiffusive accelerated convergence rates, matching the rates obtained by shift-register methods.

The acceleration scheme embodied by Min-Sum Splitting for the consensus problem bears similarities with lifted Markov chains techniques and with multi-step first order methods in convex optimization.

One can handle constraints by maximizing a penalized log-likelihood.

Penalties such as the lasso are effective in high dimensions but often lead to severe shrinkage.

This paper explores instead penalizing the squared distance to constraint sets.

Distance penalties are more flexible than algebraic and regularization penalties, and avoid the drawback of shrinkage.

To optimize distance penalized objectives, we make use of the majorization-minimization principle.

Resulting algorithms constructed within this framework are amenable to acceleration and come with global convergence guarantees.

Applications to shape constraints, sparse regression, and rank-restricted matrix regression on synthetic and real data showcase the strong empirical performance of distance penalization, even under non-convex constraints.

When recording from a population of neurons, it is usually not possible to find a single stimulus that maximizes the firing rates of all neurons.

This motivates optimizing an objective function that takes into account the responses of all recorded neurons together.

In simulations, we first confirmed that population objective functions elicited more diverse stimulus responses than single-neuron objective functions.

Then, we tested Adept in a closed-loop electrophysiological experiment in which population activity was recorded from macaque V4, a cortical area known for mid-level visual processing.

To predict neural responses, we used the outputs of a deep convolutional neural network model as feature embeddings.

Images chosen by Adept elicited mean neural responses that were 20% larger than those for randomly-chosen natural images, and also evoked a larger diversity of neural responses.

Such adaptive stimulus selection methods can facilitate experiments that involve neurons far from the sensory periphery, for which it is often unclear which stimuli to present.

In particular, our bounds exploit nonbacktracking walks, Fortuin-Kasteleyn-Ginibre FKG type inequalities, and are computed by message passing algorithms.

Nonbacktracking walks have recently allowed for headways in community detection, and this paper very ã¹ãã€ã²ãŒã 2 brilliant that their use can also impact the influence computation.

Further, we provide parameterized versions of the bounds that control the trade-off between the efficiency and the accuracy.

Finally, the tightness of the bounds is illustrated with simulations on various network ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã />Though most studies consider data stream with fixed features, in real practice the features may be evolvable.

For example, features of data gathered by limited lifespan sensors will change when these sensors are substituted by new ones.

In this paper, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur.

Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance.

Specifically, we learn two models from the recovered features and the current features, respectively.

To benefit from the recovered features, we develop two ensemble methods.

In the this web page method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved.

In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches.

Experiments on both synthetic and real data validate the effectiveness of our proposal.

This formulation arises naturally when decisions are restricted by stochastic environments or deterministic environments with noisy observations.

It also includes many important problems as special case, such as OCO with long term constraints, stochastic constrained here optimization, and deterministic constrained convex optimization.

Experiments on a real-world data center scheduling problem further verify the performance of the new algorithm.

Most techniques have focused æ©PCã²ãŒã çµ¶è³ããŠã³ããŒã local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees.

In this paper, we study kernels that are invariant to a unitary group while having theoretical guarantees in addressing the important practical issue of unavailability of transformed versions of labelled data.

A problem we call the Unlabeled Transformation Problem which is a special form of semi-supervised learning and one-shot learning.

We present a theoretically motivated alternate approach to the invariant kernel SVM based on which we propose Max-Margin Invariant Features MMIF to solve this problem.

As an illustration, we design an framework for face recognition and demonstrate the efficacy of our approach on a large scale semi-synthetic dataset with 153,000 images and a new challenging protocol on Labelled Faces in the Wild LFW while out-performing strong baselines.

However, most existing methods are built on least squares with the mean square error MSE criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise.

In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint.

A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise.

On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization.

The basic idea of TranSync is to apply truncated least squares, where the solution at each step is used to gradually prune out noisy measurements.

We analyze TranSync under both deterministic and randomized noisy models, demonstrating its robustness and stability.

Experimental results on topic ã¯ã€ã«ãã«ãŒãã²ãŒã time and real datasets show that TranSync is superior to state-of-the-art convex formulations in terms of both efficiency and accuracy.

Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model.

This work focuses on unifying two of the most widely used link-formation models: the stochastic block model SBM and the small world or latent space model SWM.

Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã for both models.

When the network comes from an SBM, the algorithm outputs a block structure.

When it is from an SWM, the algorithm outputs estimates of each node's latent position.

The proof is based on simultaneously estimating the distance from a pair of primal dual iterates to the optimal primal and dual solution set by certain residuals.

In this paper, we address a dueling bandit problem based on a cost function over a continuous ã¿ãã¬ããçšã²ãŒã ãç¡æã§ããŠã³ããŒãããæ¹æ³ />Then, we clarify the equivalence between regret minimization in dueling bandit and convex optimization for the cost function.

Moreover, considering a lower bound in convex optimization, it is turned out that our algorithm achieves the optimal convergence rate in convex optimization and the optimal regret in dueling bandit except for a logarithmic factor.

something ãžã§ããã³ãŒã¹ã¿ãŒã®å€§ç©ã®ã²ãŒã ãç¡æã§ããŠã³ããŒã congratulate, she has to identify the value of a new instance as accurately as possible.

In this work, we initiate the study of strategic predictions in machine learning.

We consider a regression task tackled by two players, where the payoff of each player is the proportion of the points she predicts more accurately than the other player.

We first revise the ã¹ããããã·ã³ãµã€ã approximately correct learning framework to deal with the case of a duel between two predictors.

We then devise an algorithm which finds a linear regression predictor that is a best response to any not necessarily linear regression algorithm.

We show that it has linearithmic sample complexity, and polynomial time complexity when the dimension of the instances domain is fixed.

We also test our approach in a high-dimensional setting, and show it significantly defeats classical regression algorithms in the prediction duel.

Together, our work introduces a novel machine learning task that lends itself well to current competitive online settings, provides its theoretical foundations, and illustrates its applicability.

In this work, we propose TernGrad that uses ternary gradients to accelerate distributed deep learning in data parallelism.

Our approach requires only three numerical levels {-1,0,1}, which can aggressively reduce the communication time.

We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients.

Guided by the bound, we propose layer-wise ternarizing and gradient clipping to improve its convergence.

Our experiments show that applying TernGrad on AlexNet does not incur any accuracy loss and can even improve accuracy.

The accuracy loss of GoogLeNet induced by TernGrad is less than 2% on average.

Finally, a performance model is proposed to study the scalability of TernGrad.

Experiments show significant speed gains for various deep neural networks.

Our source code is available.

Specifically, we develop a three-way connection for the linear propagation model, which a formulates a sparse transformation matrix where all elements can be the output from a deep CNN, but b results in a dense affinity matrix that is effective to model any task-specific pairwise similarity.

Instead of designing the similarity kernels according to image features of two points, we can directly output all similarities in a pure data-driven manner.

The spatial propagation network is a generic framework that can be applied to numerous tasks, which traditionally benefit from designed affinity, e.

Furthermore, the model can also learn semantic-aware affinity for high-level vision tasks due to the learning capability of the deep model.

We validate the proposed framework by refinement of object segmentation.

Experiments on the HELEN face parsing and PASCAL VOC-2012 semantic segmentation tasks show that the spatial propagation network provides general, effective and efficient solutions for generating high-quality segmentation results.

First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension.

Next, in an average-case and noise-free setting where article source responses exactly correspond to a linear function of i.

Finally, lower bounds on the signal-to-noise ratio are established for approximate recovery of the unknown linear function by any estimator.

We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of features specific to that hypothesis.

For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait.

We have a rich set of features for each variant e.

However popular testing approaches, such as Benjamini-Hochberg's procedure BH and independent hypothesis weighting IHWeither iPhoneçšã¯ã€ãã¯ãããã¹ãããã¢ããª these features or assume that the features are categorical.

We propose a new algorithm, NeuralFDR, which automatically learns a discovery threshold as a function of all the hypothesis features.

We parametrize the discovery threshold as a neural network, which enables flexible handling of multi-dimensional discrete and continuous features as well as efficient end-to-end optimization.

We prove that NeuralFDR has strong false discovery rate FDR guarantees, and show that it makes substantially more discoveries in synthetic and real datasets.

Moreover, we demonstrate that the learned discovery threshold is directly interpretable.

Prediction cost can be drastically reduced if the learned predictor is constructed such that on the majority of the inputs, it uses cheap features and fast evaluations.

The main challenge is to do so with little loss in accuracy.

In this work we propose a budget-aware strategy based on deep boosted regression trees.

In contrast to previous approaches to learning with cost penalties, our method can grow very deep trees that on average are nonetheless cheap to compute.

We evaluate our method click at this page a number of datasets and find that it outperforms the current state of the art by a large margin.

Our algorithm is easy to implement and its learning time is comparable to that of the original gradient boosting.

Under a probabilistic setting for discrete input spaces, we focus on the rule realization problem which generates input sample distributions that follow the given rules.

More ambitiously, we go beyond a mechanical realization that takes whatever is given, but instead ask for proactively selecting reasonable rules to realize.

This goal is demanding in practice, since https://money-free-jackpot.site/1/327.html initial rule set may not always be consistent and thus intelligent compromises are needed.

We formulate both rule realization and selection as two strongly connected components within a single and symmetric bi-convex problem, and derive an efficient algorithm that works at large scale.

Taking music compositional rules as the main example throughout the paper, we demonstrate our model's efficiency in not only music realization composition but also music interpretation and understanding analysis.

This algorithm is derived from sample compression bounds and enjoys the statistical advantages of tight, fully empirical generalization bounds, as well as the algorithmic advantages of a faster runtime and memory savings.

We prove that this algorithm is strongly Bayes-consistent in metric spaces with finite doubling dimension --- the first consistency result for an efficient nearest-neighbor sample compression scheme.

Rather surprisingly, we discover that this algorithm continues to be Bayes-consistent even in a certain infinite-dimensional setting, in which the basic measure-theoretic conditions on which classic consistency proofs hinge are violated.

This is all the more surprising, since it is known that k-NN is not Bayes-consistent in this setting.

We pose several challenging open problems for future research.

Sometimes this is in the form of a bound on the payoffs, or the knowledge of a variance or subgaussian parameter.

The results derived in these specialised cases are generalised here to the non-parametric setup, where the learner knows only a bound on the kurtosis of the noise, which is a scale free measure of the extremity of outliers.

Since read more features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers.

This paper presents Multilinear Relationship Networks MRN that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks.

By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer.

Experiments show link MRN yields state-of-the-art results on three multi-task learning datasets.

Unlink previous methods, DHA is not limited by a restricted fixed kernel function.

Further, it uses a parametric approach, rank-m Singular Value Decomposition SVDand stochastic gradient descent for optimization.

Therefore, DHA has a suitable time complexity for large datasets, and DHA does not require the training data when it computes the functional alignment for a new subject.

Experimental studies on multi-subject fMRI analysis confirm that the DHA method achieves superior performance to other state-of-the-art HA algorithms.

In the offline optimization setting, our derived methods are shown to obtain favourable adaptive guarantees which depend on the harmonic sum of the queried gradients.

We further show that our methods implicitly adapt to the objective's structure: in the smooth case fast convergence rates are ensured without any prior knowledge of the smoothness parameter, while still maintaining guarantees in the non-smooth setting.

Our approach has a natural extension to the stochastic setting, resulting in a lazy version of SGD stochastic GDwhere minibathces are chosen adaptively depending on the magnitude of the gradients.

Thus providing a principled approach towards choosing minibatch sizes.

Unfortunately, these techniques are unable to deal with stochastic perturbations of input data, induced for example by data augmentation.

In such cases, the objective is no longer a finite sum, and the main candidate for optimization is the stochastic gradient descent method SGD.

In this paper, we introduce a variance reduction approach for these settings when the objective is composite and strongly convex.

The convergence rate outperforms SGD with a typically much smaller constant factor, which depends on the variance of gradient estimates only due to perturbations on a single example.

Methods from topological data analysis, e.

However, such topological signatures often come with an unusual structure e.

While many strategies have been proposed to map these topological signatures into machine learning compatible representations, they suffer from being agnostic to the target learning task.

In contrast, we propose a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training.

Our approach is realized as a novel input layer with favorable theoretical properties.

Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin.

Predicting user activities based on point processes is a central problem.

However, existing works are mostly problem specific, use heuristics, or simplify the stochastic nature of point processes.

In this paper, we propose a framework that provides an unbiased estimator of the probability mass function of point processes.

In particular, we design a key reformulation of the prediction problem, and further derive a differential-difference equation to compute a conditional probability mass function.

Our framework is applicable to general point processes and prediction tasks, and achieves superb predictive and efficiency performance in diverse real-world applications compared to state-of-arts.

Our variant allows for tighter convergence bounds for extreme values of the CDF.

We apply our bound in the context of revenue learning, which is a well-studied problem in economics and algorithmic game theory.

For uniform convergence in the limit, we give a complete characterization and a zero-one law: if the first moment of the valuations is finite, then uniform convergence almost surely occurs; conversely, if the first moment is infinite, then uniform convergence almost never occurs.

The model based on the Poisson Factor Analysis method captures dependence among time steps by neural networks, representing the implicit distributions.

Local complicated relationship is obtained from local implicit distribution, and deep latent structure is exploited to get the long-time dependence.

Variational inference on latent variables and gradient descent based on the loss functions derived from variational distribution is performed in our inference.

Synthetic datasets and real-world datasets are applied to the proposed model and our results show good predicting and fitting performance with interpretable latent structure.

However, if its model is very flexible, empirical risks on training data will go negative, https://money-free-jackpot.site/1/344.html we will suffer from serious overfitting.

In this paper, we propose a non-negative risk estimator for PU learning: when getting minimized, it is more robust against overfitting, and thus we are able to use very flexible models such as deep neural networks given limited P data.

Moreover, we analyze the bias, consistency, and mean-squared-error reduction of the proposed risk estimator, and bound the estimation error of the resulting empirical risk minimizer.

Experiments demonstrate that our risk estimator fixes the overfitting problem of its unbiased counterparts.

We examine an M-wise comparison model that builds on the Plackett-Luce PL model where for each sample, M items are ranked according to their perceived utilities modeled as noisy observations of their underlying true utilities.

As our result, we characterize the minimax optimality on the sample size for top-K ranking.

The optimal sample size turns out to be inversely proportional to M.

We devise an algorithm that effectively converts M-wise samples into pairwise ones and employs a spectral method using the refined data.

In contrast, although it is valid in slightly restricted regimes, our result demonstrates a spectral method alone to be sufficient for the general M-wise model.

Moreover, running our algorithm on real-world data, we find that its applicability extends to settings that may not fit the PL model.

For example, would a patient's disease progression slow down if I were to go here them a dose of drug A?

Ideally, we answer our question using an experiment, but this is not always possible e.

As an alternative, we can use non-experimental data to learn models that make counterfactual predictions of what we would observe had we run an experiment.

In this paper, we propose the counterfactual GP, a counterfactual model of continuous-time trajectories time series under sequences of actions taken in continuous-time.

We develop our model within the potential outcomes framework of Neyman and Rubin.

The counterfactual GP is trained using a joint maximum likelihood objective that adjusts for dependencies between observed actions and outcomes in the training data.

We report two sets of experimental results using the counterfactual GP.

The first shows that it can be used to learn the natural progression i.

In the second, we show how the CGP can be used for medical decision support by learning counterfactual models of renal health under different types of dialysis.

A fundamental barrier when parallelizing SGD is the high bandwidth cost of communicating gradient updates between nodes; consequently, several lossy compresion heuristics have been proposed, by which nodes only communicate quantized gradients.

Although effective in practice, these heuristics do not always guarantee convergence, and it is not clear whether they can be improved.

In this paper, we propose Quantized SGD QSGDa family of compression schemes for gradient updates which provides convergence guarantees.

We show that this trade-off is inherent, in the sense that improving it past u ç¡æã®ãªã³ã©ã€ã³ã¹ããã4 threshold would violate information-theoretic lower bounds.

QSGD guarantees convergence for convex and non-convex objectives, under asynchrony, and can be extended to stochastic variance-reduced techniques.

When applied to training deep neural networks for image classification and automated speech recognition, QSGD leads to significant reductions in end-to-end training time.

For example, on 16GPUs, we can train the ResNet152 network to full accuracy on ImageNet 1.

This allows us to develop a block coordinate descent BCD training algorithm consisting of a sequence of numerically well-behaved convex optimizations.

Using ideas from proximal point methods in convex analysis, we prove that this BCD algorithm will converge globally to a stationary point with R-linear convergence rate of order one.

In experiments with the MNIST database, DNNs trained with this BCD algorithm consistently yielded better test-set error rates than identical DNN architectures trained via all the stochastic gradient descent SGD variants in the Caffe toolbox.

These methods update the weights using their gradient, estimated from a small fraction of the training data.

It has been observed that when using large batch sizes there is a persistent degradation in generalization performance - known as the ""generalization gap"" phenomena.

Identifying the origin of this gap and closing it had remained an open problem.

Contributions: We examine the initial high learning rate training phase.

We find that the weight distance from its initialization grows logarithmicaly with the number of weight updates.

We therefore propose a ""random walk on random landscape"" statistical ã«ãžããã€ã€ã«ã»ã³ãããŒãã³ which is known to exhibit similar ""ultra-slow"" diffusion behavior.

Following this hypothesis we conducted experiments to show empirically that the ""generalization gap"" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used.

We further investigate different techniques to train models in the large-batch regime and present a novel algorithm named ""Ghost Batch Normalization"" which enables significant decrease in the generalization gap without increasing the number of updates.

To validate our findings we conduct several additional experiments on MNIST, CIFAR-10, CIFAR-100 and ImageNet.

Finally, we reassess common practices and beliefs concerning training of deep models and suggest they may not be optimal to achieve good generalization.

Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning.

Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem.

Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.

Flexpoint tensors have a shared exponent that is dynamically adjusted to minimize overflows and maximize available dynamic range.

We demonstrate that 16-bit Flexpoint closely matches 32-bit floating point in training all three models, without any need for tuning of model hyperparameters.

Our results suggest Flexpoint as a promising numerical format for future hardware for training and inference.

For many probabilistic models, computation of the marginal likelihood is challenging, because it involves a sum or integral over an enormous parameter space.

Markov chain Monte Carlo MCMC is a powerful approach to compute marginal likelihoods.

Various See more algorithms and evidence estimators have been proposed in the literature.

Here we discuss the use of nonequilibrium techniques for estimating the marginal likelihood.

Nonequilibrium estimators build on ã©ã«ãžãã®æ¬ developments in statistical physics and are known as click to see more importance sampling AIS and reverse AIS in probabilistic machine learning.

We introduce estimators for the model evidence that combine forward and backward simulations and show for various challenging models that the evidence estimators outperform forward and reverse AIS.

Most existing structures e.

We derive an asymptotic instance-specific regret lower bound for these problems, and develop OSSB, an algorithm whose regret matches this fundamental limit.

Recently, neural networks have been applied to this problem with promising results.

By exploiting massively parallel GPU processing architectures and oodles of training data, they can run orders of magnitude faster than existing techniques.

However, these methods are largely unprincipled black boxes that are difficult to train and often-times specific to a single measurement matrix.

Taking inspiration from this work, we develop a novel neural network architecture that mimics the behavior of the denoising-based approximate message passing D-AMP algorithm.

The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance.

Most importantly, it outperforms the state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and run time.

Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing.

In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation.

A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the here sentence with deliberation.

Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence.

Experiments on neural machine translation and text summarization see more the effectiveness of the proposed deliberation networks.

On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.

We perform exact clustering with high probability using a convex semidefinite estimator that interprets as a corrected, relaxed version of K-means.

The estimator is analyzed through a non-asymptotic framework and showed to be optimal or near-optimal in recovering the partition.

We present a comprehensive analysis ãµãã³ãã²ãŒã ãã³ã¬ such skewness, examine its factors and impacts through both theoretical and empirical results, and discuss the possible ways to reduce its undesirable effects.

Each time the human is check this out, the agent is provided a demonstration of the desired behavior by the human.

We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.

Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives.

Our findings suggest that the convergence of current algorithms suffers due to two factors: i presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii eigenvalues with big imaginary part.

Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties.

Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes.

Bayesian optimization BO has been successfully applied to solving expensive black-box problems in engineering and machine learning.

Here we explore whether BO can be applied as a general tool for model fitting.

First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search BADSthat achieves competitive performance with an affordable computational overhead for the running time of typical models.

We then perform an extensive benchmark of BADS vs.

The algorithm is based on branch and bound and integrates dynamic programming for both domain pruning and for obtaining strong bounds for search-space pruning.

Empirically, we show that the approach dominates in terms of running times a recent integer programming approach and thereby also a recent constraint optimization approach for the problem.

Furthermore, our algorithm scales at times further with respect to the number of variables than a state-of-the-art dynamic programming algorithm for the problem, with the potential of reaching 20 variables and at the same time circumventing the tight exponential lower bounds on memory consumption of the pure dynamic programming approach.

This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function.

In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community.

We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor.

This is the first result that formally relates the revenue gained to the quality of https://money-free-jackpot.site/1/1980.html standard machine learned model.

The novel techniques distinguish themselves from prior works by the inclusion of a fresh re weighting regularization.

Extensive numerical tests using both synthetic data and real images corroborate its improved signal recovery performance and computational efficiency relative to state-of-the-art approaches.

Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables.

Bayesian optimization BO is a popular way to tackle optimization problems with expensive objective function evaluations, but has mostly been applied to unconstrained problems.

Several BO approaches have been ãã©ãŽã³ããŒã«ã²ãŒã ããŠã³ããŒã to address expensive constraints but are limited to greedy strategies maximizing immediate reward.

To address this limitation, we propose a lookahead approach that selects the next evaluation in order to maximize the long-term feasible reduction of the objective function.

We present numerical experiments demonstrating the performance improvements of such a lookahead approach compared to several greedy BO algorithms, including constrained expected improvement EIC and predictive entropy search with constraint PESC.

Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification.

In this paper we present a hierarchical approach to methods of moments to circumvent such limitations.

Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization.

Experiments on see more modeling show that our method outperforms previous tensor decomposition methods in terms of ééã¹ããã and model quality.

Existing algorithms generally formulate the task as selection of the solution from a set of bounding box proposals obtained from deep net based systems.

In this work, we demonstrate that we can cast the problem of textual grounding into a unified framework that permits efficient search over all possible bounding boxes.

Hence, we able to consider significantly more proposals and, due to the unified formulation, our approach does not rely on a successful first stage.

Beyond, we demonstrate that the trained parameters of our model can be used as word-embeddings which capture spatial-image relationships and provide interpretability.

Lastly, our approach outperforms the current state-of-the-art methods on the Flickr 30k Entities and the ReferItGame dataset by 3.

Once the due bias is enforced analytically, neither the optimization of bias terms nor the sophisticated batch that ç¡æ³è ã²ãŒã ãã¬ã€ with is needed.

Also in the light of generalized hamming distance, the popular rectified linear units ReLU can be treated as setting a minimal hamming distance threshold between network inputs and weights.

This thresholding scheme, on the one hand, can be improved by introducing double-thresholding on both positive and negative extremes of neuron outputs.

On the other hand, ReLUs turn out to be non-essential and can be removed from networks trained for simple tasks like MNIST classification.

The proposed generalized hamming network GHN as such not only lends itself to rigorous analysis and interpretation within the fuzzy logic theory but also demonstrates fast learning speed, well-controlled behaviour and state-of-the-art performances on a variety of learning tasks.

In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization and an efficient alternating gradient descent algorithm with hard thresholding to solve it.

Our algorithm is orders of magnitude faster than the convex relaxation based methods for LVGGM.

In addition, we prove that our algorithm is guaranteed to linearly converge to the unknown sparse and low-rank components up to the optimal statistical precision.

Experiments on both synthetic and genomic data demonstrate the superiority of our algorithm over the state-of-the-art algorithms and corroborate our theory.

However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples.

We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference.

We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch.

Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.

At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency.

However, studies have mainly been confined to generative tasks such as image synthesis.

In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic algorithm.

Steganography is a collection of techniques for concealing the existence of information by embedding it within a non-secret medium, such as cover texts or images.

We show that adversarial training can produce robust steganographic techniques: our unsupervised training scheme produces a steganographic algorithm that competes with state-of-the-art steganographic techniques.

We also show that supervised training of our adversarial model produces a robust steganalyzer, which performs the discriminative task of deciding if an image contains secret information.

We define a game between three parties, Alice, Bob and Eve, in order to simultaneously train both a steganographic algorithm and a steganalyzer.

Alice and Bob attempt to communicate a secret message contained within an image, while Eve eavesdrops on their conversation and attempts to determine if secret information is embedded within the image.

We represent Alice, Bob and Eve by neural networks, and validate our scheme on two independent image pity, hoyleã¹ããŒãã«ãŒãã²ãŒã sorry, showing our novel method of studying steganographic problems is surprisingly competitive against established steganographic techniques.

Despite the recent introduction of several algorithms with good empirical performance, it is unknown whether general optimal transport distances can be approximated in near-linear time.

This paper demonstrates that this ambitious goal is in fact achieved by Cuturi's Sinkhorn Distances.

This result relies on a new analysis of Sinkhorn iterations, which also directly suggests a new greedy coordinate descent algorithm Greenkhorn with the same theoretical guarantees.

Numerical simulations illustrate that Greenkhorn significantly outperforms the classical Sinkhorn algorithm in practice.

We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder.

For example, by imposing a Gaussian distribution as the prior, we can achieve a global vs.

We further show how the PixelGAN autoencoder with a categorical prior can be directly used in semi-supervised settings and achieve competitive semi-supervised classification results on the MNIST, SVHN and NORB datasets.

Most previous methods have focused on the case where tasks relations can be modeled as linear operators and regularization approaches can be used successfully.

However, in practice assuming the tasks to be linearly related is often restrictive, and allowing for nonlinear structures is a challenge.

In this paper, we tackle this issue by casting the problem within the framework of structured prediction.

Our main contribution is a novel algorithm for learning multiple tasks which are related by a system of nonlinear equations that their joint outputs need to satisfy.

We show that our algorithm can be efficiently implemented and study its generalization properties, proving universal consistency and learning rates.

Our theoretical analysis highlights the benefits of non-linear multitask learning over learning the tasks independently.

Encouraging experimental results show the benefits of the proposed method in practice.

Dictionary learning and specifically alternating minimization algorithms for dictionary learning are well studied both theoretically and empirically.

This not only allows us to get convergence rates for the error of the estimated dictionary measured in the matrix infinity norm, but also ensures that a random initialization will provably converge to the global optimum.

Our guarantees are under a reasonable generative model that allows for dictionaries with growing operator norms, and can handle an arbitrary level of overcompleteness, while having sparsity that is information theoretically optimal.

We also establish upper bounds on the sample complexity of our algorithm.

We study this problem in the high-dimensional regime where the number of observations are fewer than the dimension of the weight vector.

We assume continue reading the weight vector belongs to some closed set convex or nonconvex which captures known side-information about its structure.

We focus on the realizable model where the inputs are chosen i.

~from a Gaussian distribution and the labels are generated according to a planted weight vector.

Our results on the dynamics of convergence of these very shallow neural nets may provide some insights towards understanding the dynamics of deeper architectures.

This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f -divergence to be undefined.

We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN training procedure.

We demonstrate the effectiveness of this regularizer accross several architectures trained on common benchmark image generation tasks.

Our regularization turns GAN models into reliable building blocks for deep learning.

The combination of high-resolution observations, expressive dynamic models, and efficient machine learning algorithms can lead to crucial insights into complex interaction dynamics and the functions of these systems.

In this paper, we formulate the dynamics of a complex interacting network as a stochastic process driven by a sequence of events, and develop expectation propagation æºåž¯é»è©±çšwwe 2k15ã²ãŒã ãç¡æã§ããŠã³ããŒã to make inferences from noisy observations.

To avoid getting stuck at a local optimum, we formulate the problem of minimizing Bethe free energy as a constrained primal problem and take advantage of the concavity of dual problem in the feasible domain of dual variables guaranteed by duality theorem.

Our expectation propagation algorithms demonstrate better performance in inferring the interaction dynamics in complex transportation networks than competing models such as particle filter, extended Kalman filter, and deep neural networks.

Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s.

PILCO evaluates policies by planning state-trajectories using a dynamics model.

However, PILCO applies policies to the observed state, therefore planning in observation space.

We extend PILCO with filtering to instead plan in belief space, consistent with partially observable Markov decisions process POMDP planning.

This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original unfiltered PILCO algorithm.

We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control.

This paper is about a novel model-free IRL approach that, differently from most of the existing IRL algorithms, does not require to specify a function space where to search for the expert's reward function.

Leveraging on the fact that the policy gradient needs to be zero for any optimal policy, the algorithm generates a set of basis functions that span the subspace of reward functions that make the policy gradient vanish.

Within this subspace, using a second-order criterion, we search for the reward function that penalizes the most a deviation from the expert's policy.

After introducing our approach for finite domains, we extend it to continuous ones.

The proposed approach is empirically compared to other IRL methods both in the finite Taxi domain and in the continuous Linear Quadratic Gaussian LQG and Car on the Hill environments.

In contrast to traditional methods that attempt to solve the ERM problem corresponding to the full dataset directly, adaptive sample size schemes start with a small number of samples and solve the corresponding ERM problem to its statistical accuracy.

The sample size is then grown geometrically -- e.

Theoretical analyses show that the use of adaptive sample size methods reduces the overall computational cost of achieving the statistical accuracy of the whole dataset for a broad range of deterministic and stochastic first-order methods.

The gains are specific to the choice of method.

When particularized to, e.

Numerical experiments on various datasets confirm theoretical claims and showcase the gains of using the proposed adaptive sample size scheme.

Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image.

In this study, we attempt to place a full size color image within another image of the same size.

Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair.

The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources.

Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions.

Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits.

This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks.

Specifically, we propose two novel approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios.

In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning.

To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language.

Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer.

We also analyze the relative performance of the two approaches and study conditions in which they perform best.

In particular, meta induction outperforms all existing approaches under extreme data sparsity when a very small number of examples are availablei.

For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.

Among these, regression trees and their ensembles have demonstrated impressive empirical performance.

In this work, we shed light on the machinery behind Bayesian variants of these methods.

In particular, we study More info regression histograms, such as Bayesian dyadic trees, in the simple regression case with just one predictor.

We focus on the reconstruction of regression surfaces that are piecewise constant, where the number of jumps is unknown.

We show that with suitably designed priors, posterior distributions concentrate around the true step regression function at a near-minimax rate.

Thus, Bayesian dyadic regression trees are fully adaptive and can recover the true piecewise regression function nearly as well as if we knew the exact number and location of jumps.

Our results constitute the first step towards understanding why Bayesian trees and their ensembles have worked so well in practice.

As an aside, we discuss prior distributions on balanced interval partitions and how they relate to an old problem in geometric probability.

Namely, we relate the probability of covering the circumference of a circle with random arcs whose endpoints are confined to a grid, a new variant of the original problem.

However, go here the richness of such interactions trades off against the ability of a network to simultaneously carry out multiple independent processes -- a salient limitation in many domains of human cognition -- remains largely unexplored.

On the positive side, we demonstrate that networks that are random-like e.

Our results shed light into the parallel-processing limitations of neural systems and provide insights that may be useful for the analysis and design of parallel architectures.

The area of robust learning and optimization has generated a significant amount of interest in the learning and statistics communities in recent years owing to its applicability in scenarios with corrupted data, as well as in handling model mis-specifications.

In particular, special interest has been devoted to the fundamental problem of robust åŠç²Ÿãªã³ã©ã€ã³ã²ãŒã regression where estimators that can tolerate corruption in up to a constant fraction of the response variables are widely studied.

Surprisingly however, to this date, we are not aware of a polynomial time estimator that offers a consistent estimate in the presence of dense, unbounded corruptions.

In this work we present such an estimator, called CRR.

This solves an open problem put forward in the work of Bhatia et al, 2015.

Our consistency analysis requires a novel two-stage proof technique involving a careful analysis of the stability of ordered lists which may be of independent interest.

We show that CRR not only offers consistent estimates, but is empirically far superior to several other recently proposed algorithms for the robust regression problem, including extended Lasso and the TORRENT algorithm.

In comparison, CRR offers comparable or better model recovery but with runtimes that are faster by an order of magnitude.

However, in reinforcement learning domains with sparse rewards, value functions have non-smooth structure with a characteristic asymmetric discontinuity whenever rewards arrive.

We propose a mechanism that learns an interpolation between a direct value estimate and a projected value estimate computed from the encountered reward and the previous estimate.

This reduces the need to learn about discontinuities, and thus improves the value function approximation.

Furthermore, as the interpolation is learned and state-dependent, our method can deal with heterogeneous observability.

We demonstrate that this one change leads to significant improvements on multiple Atari games, when applied to the state-of-the-art A3C algorithm.

In this setting, arms may not be comparable, and there may be several incomparable optimal arms.

We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms, or Pareto front, of any poset even when pairs of comparable arms cannot be a priori distinguished from pairs of incomparable arms, with a set of minimal assumptions.

This means that UnchainedBandits does not require information about comparability and can be used with limited knowledge of the poset.

To achieve this, the algorithm relies on the concept of decoys, which stems from social psychology.

We also provide theoretical guarantees on both the regret incurred and the number of comparison required by UnchainedBandits, and we report compelling empirical results.

Specifically, we introduce models based on elementary symmetric polynomials; these polynomials capture ""partial volumes"" and offer a graded interpolation between the widely used A-optimal ãµãã«ãŒç±ã¹ããããã·ã³ D-optimal design models, obtaining each of them as special cases.

ããŒã«ãŒæŠè¡é

## ãã«ãžãè±äŒè©±ããšãã«ãžãæåãã®ææ¥çŽ¹ä» - æ¥æ¬ã«ãžãã¹ã¯ãŒã« æ¥æ¬åã®ã«ãžããã£ãŒã©ãŒå°éé€ææ©é¢ ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

## ãã«ãžãè±äŒè©±ããšãã«ãžãæåãã®ææ¥çŽ¹ä» - æ¥æ¬ã«ãžãã¹ã¯ãŒã« æ¥æ¬åã®ã«ãžããã£ãŒã©ãŒå°éé€ææ©é¢ ç©¶æ¥µã®ãããµã¹ããŒã«ãã ç·Žç¿ã²ãŒã

ä»ã§ãããªãã²âã¿ãããªæ±ãã ãã©ãã«ã¢ãŒã¬ã¯ããšããšACã§ãåšãã®ãã€ããšã¯ã€ã¯ã€èšããªããè¬è§£ãããã²ãŒã ãªãã ãã ä»ã¿ããã«ããã... 104 ïŒæ¢ã«ãã®ååã¯äœ¿ãããŠããŸãïŒ è»¢èŒã¯çŠæ¢ïŒ2016/02/14(æ¥) 13:19:16.76 ID:MwmAuXnM: ããããç·Žç¿ããã«ã.... ãåãå€©æåãç©¶æ¥µã®ç°åžžè ãšããŠèããã°æ°æã¡ãå°ãã¯çè§£ã§ããã.... è³ãã®ãªããã©ãã¯ãžã£ãã¯ããããµã¹ããŒã«ãã ãšæ¯ã¹ãªããæå³ããã ãã€ã©ã¹ãã®ç·Žç¿ãæçš¿ãç¶ããããã®ã³ãã®ãããªãã®ããŸãšããŠã¿ãŸããã ä»ã®ãžã£ã³ã«ã§ã... ãšèããããã¯ã»ãæã£ãŠããŸãïŒãããããŸãå©çšããã®ãããœãŒã·ã£ã«ã²ãŒã ã§ãïŒã ããããç¿æ £ã. çµµãæããšããããšããã®ç·Žç¿ãã²ãŒã ã®ã¬ãã«äžããšèããŸãããã RPGã§ãã. ç©¶æ¥µãªè©±ãããã®ââå çã®çµµãããããªãã®çµµã®ã»ããå¥œãã ãå¥œã¿ã ããšããäººã¯å¿ ãããã®ã§ãã ç¬ãšç«ã®.. ã²ãŒã ã1 Â· Re:ãŒãããå§ããç°äžççæŽ» ã¬ã (ãªãŒã) ã¹ãã« ã©ã (ãªãŒã) ããŒã«ãŒ ãããµã¹ããŒã«ãã .

ä»åŸã¯ãéå»ã«ã²ãŒã ã§äœ¿çšããã€ã©ã¹ãããæ¬¡åäœã®ã€ã©ã¹ããªã©ãæçš¿ããããšã«ãªãããã§ãã.. ã²ãšããšãããš 214 äžæ¥ã®åºæ¥äºãçµµã«ããŠç·Žç¿ããããšæãå§ããŸãã (ã ããã)ã¢ããã° + è¶ çµ¶ãžãããã§ãããèª°ãã«èŠãŠãããã°ç¶ãã®ããªããš im6050388.... ISIS chan æã ã®ç©¶æ¥µçãªç®æšã¯ISISã¡ããèãç»åã«ããç¡æ æ²ãªãµãžã§ã¹ãæ±æã§ãã â¡ãµãžã§ã¹ãæ±æ â¡æ€çŽ¢ã¯ãŒãæ±æ â¡ç»åæ€çŽ¢ã®æ±æ.... ãããµã¹ããŒã«ãã ããã¬ã€ãã3äººãæããŸããã http://seiga.nicovideo.jp/seiga/im6059609.