AIMC Topic: Probability Learning

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

Neural networks : the official journal of the International Neural Network Society Jan 27, 2025
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...
Humans Neural Networks, Computer Algorithms Deep Learning Image Processing, Computer-Assisted Probability Probability Learning
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Machine learning, statistical learning and the future of biological research in psychiatry.

Psychological medicine Jul 13, 2016
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...
Humans Machine Learning Psychiatry Biomedical Research Datasets as Topic 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 Apr 12, 2016
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...
Neural Networks, Computer Machine Learning Algorithms Learning Normal Distribution Probability Learning
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