Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.


The spike and slab Restricted Boltzmann Machine (RBM) is defined by having both a real valued ``slab'' variable and a binary ``spike'' variable associated with each unit in the hidden layer. In this paper we generalize and extend the spike and slab RBM to include non-zero means of the conditional distribution over the observed variables conditional on the binary spike variables. We also introduce a term, quadratic in the observed data that we exploit to guarantee the all conditionals associated with the model are well defined -- a guarantee that was absent in the original spike and slab RBM. The inclusion of these generalizations improves the performance of the spike and slab RBM as a feature learner and achieves competitive performance on the CIFAR-10 image classification task. The spike and slab model, when trained in a convolutional configuration, can generate sensible samples that demonstrate that the model has capture the broad statistical structure of natural images.

Questions and Answers

You need to be logged in to be able to post here.