Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Netw...
Guillaume Desjardins, Aaron C. Courville, Yoshua B...
Restricted Boltzmann Machines (RBMs) — the building block for newly popular Deep Belief Networks (DBNs) — are a promising new tool for machine learning practitioners. However,...
Sang Kyun Kim, Lawrence C. McAfee, Peter L. McMaho...
The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We ...
Restricted Boltzmann Machines (RBMs) are a type of probability model over the Boolean cube {-1, 1}n that have recently received much attention. We establish the intractability of ...
Although there are many neural network FPGA architectures, there is no framework for designing large, high-performance neural networks suitable for the real world. In this paper, ...