Monday, August 30, 2010
Replicated Softmax: an undirected topic model
This paper talks about an application of restricted Boltzmann machine. The visible layer contains a matrix of 0-1 variables (indicating whether the kth sample takes value i). And the hidden layer is again binary variables. After defining the conditional probability (since the conditional probability for visible layer is multinomial and therefore a softmax function on the hidden units, that's where the model's name comes from; replicated for the weight matrix is shared), we may use contrastive divergence to train the model. In topic model, the so-called log-perplexity (probability of observing testing samples) has to be computed so as to compare with other topic models (such as LDA). Annealed importance sampling is employed to compute the partition function.
There is some link of this model with semantic hash previously developed by the authors. Experiments shows favorable result with undirected topic model.
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