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Probability Learning

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Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

Neural networks : the official journal of the International Neural Network Society
The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learnin...

Machine learning, statistical learning and the future of biological research in psychiatry.

Psychological medicine
Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other 'omic' measures. The analysis of these dataset...

Deep one-class probability learning for end-to-end image classification.

Neural networks : the official journal of the International Neural Network Society
One-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negative samples with a model trained via only positive samples or one-class annotated samples. With the d...