Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments. We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees. We apply our method to model-free multiagent reinforcement learning in adversarial sequential decision problems (zero-sum imperfect information games), using RL-style function approximation. We evaluate on commonly used benchmark Poker domains, showing performance against fixed policies and empirical convergence to approximate Nash equilibria in self-play with rates similar to or better than a baseline model-free algorithm for zero-sum games, without any domain-specific state space reductions.

For today's leading deep learning methods and technology, attend the conference and training workshops at Predictive Analytics World for Healthcare, June 16-19, 2019 in Las Vegas. Moving patient data online has been a great boon for the practice of medicine. Patient records, formerly pieces of paper in a folder, are transitioning to electronic health records, or EHRs. While this has done wonders for transferring records between offices and aiding in connecting technology like wearables and providing big data for machine learning, the quantity also raises questions of patient privacy and data security. The start of this story is in the volume of data.

Understanding clothes and broad fashion products from such an image would have huge commercial and cultural impacts on modern societies. Deploying such a technology would empower not only the fashion buyers to find what they want, but also those small and large sellers to have quicker sales with less hassle. This technology requires excellence in several computer vision tasks: what the product is in the image (image classification), where it is (object detection, semantic image segmentation, instance segmentation), visual similarity, how to describe the product and its image (image captioning), etc. Recent works in convolutional neural networks (CNNs) have significantly improved the state-of-the-art performance of those tasks. In the image classification task, ResNeXt-101 method has achieved 85.4% in top-1-accuracy1 in ImageNet-1K; in object detection, the best method2 has achieved 52.5% mAP in the COCO 2017 benchmark for generic object detection; in semantic image segmentation, the top-performing method3 has reached 89% mIOU in PASCAL VOC leaderboard for the generic object segmentation.

A team of researchers at Cyxtera Technologies has recently proposed a neural network-based method for identifying malicious use of web certificates. Their approach, outlined in a paper published in ACM Digital Library, uses the content of transport layer security (TLS) certificates to identify legitimate certificates, as well as malicious patterns used by attackers. Encryption is an increasingly popular way of securing communications and exchanges of data online so that they cannot be intercepted and accessed by third parties. Despite its many advantages, encryption also allows cybercriminals to hide their messages and avoid detection when carrying out malware attacks. Moreover, encryption can give online users a false sense of security, as many web browsers display a green lock symbol when the connection to a website is encrypted, even when these websites are actually executing phishing attacks.

An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. In this post, you will discover the LSTM Autoencoder model and how to implement it in Python using Keras. A Gentle Introduction to LSTM Autoencoders Photo by Ken Lund, some rights reserved. An autoencoder is a neural network model that seeks to learn a compressed representation of an input.

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Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile by Chin-Chia Michael Yeh Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, September 2018 Dr. Eamonn Keogh, Chairperson The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced a near universal time series data mining tool called matrix profile which solves the all-pairssimilarity-search problem and caches the output in an easy-to-access fashion. The proposed algorithm is not only parameter-free, exact and scalable, but also applicable for both single and multidimensional time series. By building time series data mining methods on top of matrix profile, many time series data mining tasks (e.g., motif discovery, discord discovery, shapelet discovery, semantic segmentation, and clustering) can be efficiently solved. Because the same matrix profile can be shared by a diverse set of time series data mining methods, matrix profile is versatile and computed-once-use-many-times data structure. We demonstrate the utility of matrix profile for many time series data mining problems, including motif discovery, discord discovery, weakly labeled time series classification, and vi representation learning on domains as diverse as seismology, entomology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring, and medicine. We hope the matrix profile is not the end but the beginning of many more time series data mining projects.

We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such as images, convolutional layers are designed by translating a convolutional kernel over all pixels, thus enforcing translation equivariance. In the case of general graphs however, translation is not a well-defined operation, which makes shifting a convolutional kernel not straightforward. In this article, we introduce a methodology to allow the design of convolutional layers that are adapted to signals evolving on irregular topologies, even in the absence of a natural translation. Using the designed layers, we build a CNN that we train using the initial set of signals. Contrary to other approaches that aim at extending CNNs to irregular domains, we incorporate the classical settings of CNNs for 2D signals as a particular case of our approach. Designing convolutional layers in the vertex domain directly implies weight sharing, which in other approaches is generally estimated a posteriori using heuristics.

We consider a simple and overarching representation for permutation-invariant functions of sequences (or set functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allows us to leverage the rich and mature literature on permutation-sensitive functions to construct novel and flexible permutation-invariant functions. If carried out naively, Janossy pooling can be computationally prohibitive. To allow computational tractability, we consider three kinds of approximations: canonical orderings of sequences, functions with k-order interactions, and stochastic optimization algorithms with random permutations. Our framework unifies a variety of existing work in the literature, and suggests possible modeling and algorithmic extensions. We explore a few in our experiments, which demonstrate improved performance over current state-of-the-art methods.

ABSTRACT This paper proposes a voice conversion method based on fully convolutional sequence-to-sequence (seq2seq) learning. The present method, which we call "ConvS2S-VC", learns the mapping between source and target speech feature sequences using a fully convolutional seq2seq model with an attention mechanism. Owing to the nature of seq2seq learning, our method is particularly noteworthy in that it allows the flexible conversion of not only the voice characteristics but also the pitch contour and duration of the input speech. The current model consists of six networks, namely source and target encoders, a target decoder, source and target reconstructors and a postnet, which are designed using dilated causal convolution networks with gated linear units. Subjective evaluation experiments revealed that the proposed method obtained higher sound quality and speaker similarity than a baseline method.