WebAug 27, 2024 · Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in machine learning, and recently found numerous applications in quantum many-body physics. We show that there are fundamental connections between them and tensor networks. In particular, we demonstrate that any … Invented by Geoffrey Hinton, a Restricted Boltzmann machine is an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. (For more concrete examples of how neural networks like RBMs can be employed, please see our page on use cases). … See more But in this introduction to restricted Boltzmann machines, we’ll focus on how they learn to reconstruct data by themselves in an unsupervised fashion (unsupervised means without ground-truth labels in a test set), … See more The variable k is the number of times you run contrastive divergence. Contrastive divergence is the method used to calculate the gradient (the slope representing the relationship between a network’s weights and … See more
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Boltzmann machines as two-dimensional tensor networks
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