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

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

J. Tubiana
Simona Cocco
R. Monasson
ArXiv (abs)PDFHTML

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

5 / 5 papers shown
Title
Pattern reconstruction with restricted Boltzmann machines
Pattern reconstruction with restricted Boltzmann machines
G. Genovese
48
0
0
14 May 2022
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
Sonia K. Murthy
Kyle Lo
Daniel King
Chandra Bhagavatula
Bailey Kuehl
Sophie Johnson
Jon Borchardt
Daniel S. Weld
Tom Hope
Doug Downey
79
12
0
14 May 2022
Generative Capacity of Probabilistic Protein Sequence Models
Generative Capacity of Probabilistic Protein Sequence Models
Francisco McGee
Quentin Novinger
R. Levy
Vincenzo Carnevale
A. Haldane
81
34
0
03 Dec 2020
Machine Learning for Condensed Matter Physics
Machine Learning for Condensed Matter Physics
Edwin Bedolla
L. C. Padierna
R. Castañeda-Priego
AI4CE
69
67
0
28 May 2020
Maximal Relevance and Optimal Learning Machines
Maximal Relevance and Optimal Learning Machines
O. Duranthon
M. Marsili
R Xie
118
0
0
27 Sep 2019