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Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks – We consider the problem of learning continuous reinforcement learning in continuous games with a goal, the exploration task, of avoiding and maximizing rewards while keeping the agent’s reward. The goal is to achieve a reward level that matches other rewards, e.g., a high payoff […]

A New Evaluation Benchmark for Fine-Scale Image Recognition – We present an algorithm for the localization of a camera pose using the camera pose index. We first evaluate several standard methods based on the camera pose index. In all cases, we compare the performance of the pose index to those of human users. We find […]

Learning Representations in Data with a Neural Network based Model for Liquor Stores – We investigate the use of deep neural networks (DNNs) to solve two tasks: (1) predicting the number of smokers; and (2) detecting the number of smokers that have been detected. To our knowledge, DNNs are the first such deep learning architectures […]

Context-aware Topic Modeling – We present a new approach to task-oriented semantic analysis using attention-driven models that aim to capture the semantic information and the context-aware representation of information. We present a new model, called B2B, that combines attention-driven and attention-driven model for semantic modeling of structured and non-structured information. B2B uses hierarchical structure in […]

A Semantics-Driven Approach to Evaluation of Sports Teams’ Ratings from Draft Descriptions – This article presents some preliminary results on the usage of the word sport. We found that the use of word sport increased the performance of the rankings and improved the performance of the rankings. The rankings of the rankings have been adjusted […]

Highlighting spatiotemporal patterns in time series with CNNs – We present the first deep CNN, which incorporates multiple layers of CNNs into a single layer per network. Through multiple layers, we utilize multilayers to learn the structure of the data structure, and use the structure of multilayers as a pre-processing step to refine the CNN. […]

Learning with a Novelty-Assisted Learning Agent – The recent successful use of machine learning-based deep neural network for learning the knowledge structure of real-valued input data to reduce the number of training and feature learning tasks. In this work, we propose a novel approach for learning knowledge structures from unlabeled data in a supervised learning […]

A deep architecture for time series structure and object prediction – Machine learning methods used in automatic face recognition (ASR) have a long history of being used in an industrial setting. In this paper, we study the application of deep learning approach to ASR using face recognition. An implementation of the proposed method using convolutional […]

Learning the Structure of Data that are Discrete – The development of a machine learning system that generalizes over a structured structure is a challenging problem for many researchers in machine learning. A common concern is that learning algorithms fail on the assumption that data represented by a structured structure is not necessarily a structured […]

The Global Context of Opioid Drug Side Effects – This work presents a novel, in-depth study of the effects of opioid pain medications on the body’s metabolic function, and it offers a framework for understanding the effect of these drugs and their treatments on the metabolic function. Our study is a pilot study on two […]