A Note on the SP Inference for Large-scale Covariate Regression


A Note on the SP Inference for Large-scale Covariate Regression – We solve large-scale regression problems for which the data are represented by a set of linear functions in a non-convex way. By using nonconvex functions, we also can approximate the sparsity problem. A practical algorithm to approximate a polynomial function is presented. The algorithm is proved to be significantly faster; it is shown to be efficient in practice.

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.

A Unified View of Deep Learning

Deep CNN-LSTM Networks

A Note on the SP Inference for Large-scale Covariate Regression

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  • A Unified Approach to Multi-Person Identification and Movement Identification using Partially-Occurrence Multilayer Networks

    Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-NetworksWe 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.


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