Learning Gaussian-Bernoulli RBMs Using Difference of Convex Functions Optimization

The Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM) is a useful generative model that captures meaningful features from the given n -dimensional continuous data. The difficulties associated with learning GB-RBM are reported extensively in earlier studies. They indicate that the training of...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-10, Vol.33 (10), p.5728-5738
Hauptverfasser:	Upadhya, Vidyadhar, Sastry, P. S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Approximation algorithms Computational modeling Contrastive divergence (CD) Convex analysis Convex functions Data models difference of convex programming (DCP) Gaussian > Bernoulli RBM (GB-RBM) Learning Machine learning Markov processes Optimization Programming restricted Boltzmann machine (RBM) Stochasticity Training
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