2014
NIPS
NeurIPS 2014
Feedforward Learning of Mixture Models
Abstract
We develop a biologically-plausible learning rule that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule within a tensor framework, substantially increasing the generality of the learning problem it solves. It achieves this by incorporating triplets of samples from the mixtures, which provides a novel information processing interpretation to spike-timing-dependent plasticity. We provide both proofs of convergence, and a close fit to experimental data on STDP.
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Keyword Pioneer
- tensor method
🐝
Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Topic Pioneer
- Representation Learning
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Interdisciplinary Bridge
- Deep Learning and Machine Learning
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Trend Setter
- Self-Supervised Learning
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Architectures > Neural Networks
Machine Learning > Core Methods > Feature Learning
Deep Learning > Learning Types > Self-Supervised Learning