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Restricted Boltzmann Machines as Models of Interacting Variables
We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distributions over binary variables depends on the hidden node activation function and on the number of hidden nodes.
On the complexity of logistic regression models
We investigate the complexity of logistic regression models which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997).
Sparse model selection in the highly under-sampled regime
We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available.
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