Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this work we give new results for learning Restricted Boltzmann Machines, probably the most well-studied class of latent variable models.

The algorithm's computational complexity is polynomial in

We will include a more detailed discussion to motivate our methodology.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a new Gibbs sampler algorithm for FHMMs. The idea is to add an auxillary variable, U, to the state of the Gibbs sampler. The value of U restricts the set of possible values that the hidden state X can take at the next step of the Gibbs sampler. As the number of possible values for X_i is small for each time point i, we can update X given U (and the data) using FFBS. I think this is an original and clever approach to an important class of problems.

Neural Information Processing Systems http://nips.cc/

Making decisions in the presence of a strategic opponent requires one to take into account the opponent's ability to actively mask its intended objective. To describe such strategic situations, we introduce the non-cooperative inverse reinforcement learning (N-CIRL) formalism. The N-CIRL formalism consists of two agents with completely misaligned objectives, where only one of the agents knows the true objective function.

Neural Information Processing Systems http://nips.cc/

The use of neural networks for function approximation has led to many recent advances in Reinforcement Learning (RL) .

Planning with a generative model is thinking before acting .