Graph Deconvolution Methods for Improved Generative Modeling – We present a framework for the prediction of the future, and the use of future data to model the outcome of the action. In the context of the task of predicting the future, we develop a Bayesian model incorporating several recent improvements to the state of the art. Our model aims to learn a Bayesian model and to infer the past state of a future state which can be estimated using the past data. The framework is evaluated for several datasets of synthetic and real-world action data generated from the Web. In the domain of human action, we show that it is possible to perform classification even under highly noisy conditions, and to estimate the best possible action at near future time, with some regret in the estimation of the past. We show that the model performs better than state of the art, but it can be used at a time when a significant amount of time is needed for human actions to be observed.

This paper presents a novel approach for training deep reinforcement learning agents to anticipate the reward of some tasks. We use supervised learning to model actions given rewards and the reward of the agents are not explicitly represented by value functions. As the goal of the proposed model is to predict the reward of the agents, it is often useful to consider rewards that can be inferred from the expected rewards. We propose the use of a novel metric called the Expectation-Maximization (EM) metric to improve the prediction performance, achieving the best expected rewards observed by the EM.

Learning to Exploit Nodes with Data at Symmetry

Unsupervised Learning with the Hierarchical Recurrent Neural Network

# Graph Deconvolution Methods for Improved Generative Modeling

Learning the Mean and Covariance of Continuous Point Processes

Deep Neural Network Training of Interactive Video Games with Reinforcement LearningThis paper presents a novel approach for training deep reinforcement learning agents to anticipate the reward of some tasks. We use supervised learning to model actions given rewards and the reward of the agents are not explicitly represented by value functions. As the goal of the proposed model is to predict the reward of the agents, it is often useful to consider rewards that can be inferred from the expected rewards. We propose the use of a novel metric called the Expectation-Maximization (EM) metric to improve the prediction performance, achieving the best expected rewards observed by the EM.