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Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, Ryan P. Adams
Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "bl…
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, clas…
Training Restricted Boltzmann Machines on Word Observations
George E. Dahl, Ryan P. Adams, Hugo Larochelle
The restricted Boltzmann machine (RBM) is a flexible tool for modeling complex data, however there have been significant computational difficulties in using RBMs to model high-dime…