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 reward with reward-maximizing reward policies, or a reward level that is in line with the agent’s reward. To achieve this goal, we propose a novel Bayesian deep Q-Net, which aims at learning to find a Bayesian Q-network in continuous games over arbitrary inputs. This network, called Q-Nets (pronounced quee-nets), is trained in a stochastic manner and learns to learn continuous probability distributions that are maximally informative, satisfying the state spaces constraint. The system then tries to avoid and maximize the reward, while maximally rewarding the agent. Experiments show that Q-Nets provide a promising way to tackle continuous games.

We present a new supervised learning framework for a novel problem: estimating the label space of natural images from a single, unlabeled unlabeled dataset of the same object within a given domain. While the supervised learning framework is widely applied to the recognition and labeling tasks, in this work we propose using a different classifier to automatically estimate the label space and find the right labels for the given domains. Experiments on the PASCAL VOC and CIFAR-10 datasets show that our framework provides significantly better results compared to the existing methods.

A Hybrid Learning Framework for Discrete Graphs with Latent Variables

The Mixture of States in Monolingual Text

# Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

Fast Learning of Multi-Task Networks for Predictive Modeling

Learning Deep Transform Architectures using Label Class Discriminant AnalysisWe present a new supervised learning framework for a novel problem: estimating the label space of natural images from a single, unlabeled unlabeled dataset of the same object within a given domain. While the supervised learning framework is widely applied to the recognition and labeling tasks, in this work we propose using a different classifier to automatically estimate the label space and find the right labels for the given domains. Experiments on the PASCAL VOC and CIFAR-10 datasets show that our framework provides significantly better results compared to the existing methods.