In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Asynchronous distributed gradient descent algorithms for training of deep neural
 networks are usually considered as inefficient, mainly because of the Gradient delay
 problem. In this paper, we propose a novel asynchronous distributed algorithm
 that tackles this limitation by well-thought-out averaging of model updates, computed
 by workers. The algorithm allows computing gradients along the process
 of gradient merge, thus, reducing or even completely eliminating worker idle time
 due to communication overhead, which is a pitfall of existing asynchronous methods.
 We provide theoretical analysis of the proposed asynchronous algorithm,
 and show its regret bounds. According to our analysis, the crucial parameter for
 keeping high convergence rate is the maximal discrepancy between local parameter
 vectors of any pair of workers. As long as it is kept relatively small, the
 convergence rate of the algorithm is shown to be the same as the one of a sequential
 online learning. Furthermore, in our algorithm, this discrepancy is bounded
 by an expression that involves the staleness parameter of the algorithm, and is
 independent on the number of workers. This is the main differentiator between
 our approach and other solutions, such as Elastic Asynchronous SGD or Downpour
 SGD, in which that maximal discrepancy is bounded by an expression that
 depends on the number of workers, due to gradient delay problem. To demonstrate
 effectiveness of our approach, we conduct a series of experiments on image
 classification task on a cluster with 4 machines, equipped with a commodity communication
 switch and with a single GPU card per machine. Our experiments
 show a linear scaling on 4-machine cluster without sacrificing the test accuracy,
 while eliminating almost completely worker idle time. Since our method allows
 using commodity communication switch, it paves a way for large scale distributed
 training performed on commodity clusters.
A method for an efficient asynchronous distributed training of deep learning models along with theoretical regret bounds.

Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function. GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data. The lack of having explicit probability models in GANs prohibits computation of sample likelihoods in their frameworks and limits their use in statistical inference problems. In this work, we show that an optimal transport GAN with the entropy regularization can be viewed as a generative model that maximizes a lower-bound on average sample likelihoods, an approach that VAEs are based on. In particular, our proof constructs an explicit probability model for GANs that can be used to compute likelihood statistics within GAN's framework. Our numerical results on several datasets demonstrate consistent trends with the proposed theory.
A statistical approach to compute sample likelihoods in Generative Adversarial Networks

Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.

We present a Bayesian inference model to infer contrastive explanations (as LTL specifications) describing how two sets of plan traces differ.