Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

DOI10.1162/NECO_A_01210zbMath1429.92113arXiv1902.06495OpenAlexW2914341699WikidataQ91508560 ScholiaQ91508560MaRDI QIDQ5214360

Simona Cocco, J. Tubiana, Remi Monasson

Publication date: 7 February 2020

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.06495

zbMATH Keywords

restricted Boltzmann machineslattice proteinslearning compositional representations

Mathematics Subject Classification ID

Protein sequences, DNA sequences (92D20) Computational methods for problems pertaining to biology (92-08)

Related Items (2)

Decreasing the Size of the Restricted Boltzmann Machine ⋮ Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

Uses Software

• Scikit
• darch
• Keras

Cites Work

• Unnamed Item
• On the equivalence of Hopfield networks and Boltzmann machines
• Thermodynamics of restricted Boltzmann machines and related learning dynamics
• Immune networks: multitasking capabilities near saturation
• Training Products of Experts by Minimizing Contrastive Divergence
• Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
• A Fast Learning Algorithm for Deep Belief Nets
• Compressed sensing

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