Self-learning Monte Carlo with deep neural networks

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Deep Convolutional Neural Networks have revolutionized Computer Go. Large networks have emerged as state-of-the-art models for move prediction and are used not only as stand-alone players but also inside Monte Carlo Tree Search to select and bias moves. Using neural networks inside the tree search is a challenge due to their slow execution time even if accelerated on a GPU. In this paper we eva...

Generalizing Hamiltonian Monte Carlo with Neural Networks

We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distri...

Self-learning Monte Carlo method

Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo

Deep latent Gaussian models are powerful and popular probabilistic models of highdimensional data. These models are almost always fit using variational expectationmaximization, an approximation to true maximum-marginal-likelihood estimation. In this paper, we propose a different approach: rather than use a variational approximation (which produces biased gradient signals), we use Markov chain M...

Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks

In this paper, we introduce an approach to automated story generation using Markov Chain Monte Carlo (MCMC) sampling. This approach uses a sampling algorithm based on Metropolis-Hastings to generate a probability distribution which can be used to generate stories via random sampling that adhere to criteria learned by recurrent neural networks. We show the applicability of our technique through ...