Crime incidents embedding using restricted Boltzmann machines

Collaborative filtering Scalable approaches using restricted Boltzmann machines

Discriminative Restricted Boltzmann Machines are Universal Approximators for Discrete Data

This report proofs that discriminative Restricted Boltzmann Machines (RBMs) are universal approximators for discrete data by adapting existing universal approximation proofs for generative RBMs. Discriminative Restricted Boltzmann Machines are Universal Approximators for Discrete Data Laurens van der Maaten Pattern Recognition & Bioinformatics Laboratory Delft University of Technology

Data Normalization in the Learning of Restricted Boltzmann Machines

In practice, training Restricted Boltzmann Machines with Contrastive Divergence and other approximate maximum likelihood methods works well on data with black backgrounds. However, when using inverted images for training, learning is typically much worse. In this paper, we propose a very simple yet very effective solution to this problem. The new algorithm requires the addition of only three(!)...

Sparse Group Restricted Boltzmann Machines

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidde...

Temporal Restricted Boltzmann Machines for Dependency Parsing

We propose a generative model based on Temporal Restricted Boltzmann Machines for transition based dependency parsing. The parse tree is built incrementally using a shiftreduce parse and an RBM is used to model each decision step. The RBM at the current time step induces latent features with the help of temporal connections to the relevant previous steps which provide context information. Our p...