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Sparse generative modeling via parameter-reduction of Boltzmann
  machines: application to protein-sequence families
v1v2v3 (latest)

Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families

Physical Review E (PRE), 2020
23 November 2020
Pierre Barrat-Charlaix
Anna Paola Muntoni
Kai Shimagaki
M. Weigt
F. Zamponi
ArXiv (abs)PDFHTML

Papers citing "Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families"

3 / 3 papers shown
Exploring the Protein Sequence Space with Global Generative Models
Exploring the Protein Sequence Space with Global Generative ModelsCold Spring Harbor Perspectives in Biology (Cold Spring Harb Perspect Biol), 2023
S. Romero-Romero
Sebastian Lindner
Noelia Ferruz
237
7
0
03 May 2023
Optimal regularizations for data generation with probabilistic graphical
  models
Optimal regularizations for data generation with probabilistic graphical models
Arnaud Fanthomme
Francesca Rizzato
Simona Cocco
R. Monasson
241
3
0
02 Dec 2021
Equilibrium and non-Equilibrium regimes in the learning of Restricted
  Boltzmann Machines
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann MachinesNeural Information Processing Systems (NeurIPS), 2021
A. Decelle
Cyril Furtlehner
Beatriz Seoane
333
37
0
28 May 2021
1
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