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pro vyhledávání: '"Thériault, Robin"'
Modelling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Setting
Restricted Boltzmann machines (RBM) are generative models capable to learn data with a rich underlying structure. We study the teacher-student setting where a student RBM learns structured data generated by a teacher RBM. The amount of structure in t
Externí odkaz:
http://arxiv.org/abs/2410.16150
Autor:
Thériault, Robin, Tantari, Daniele
Publikováno v:
SciPost Phys. 17 (2024) 040
Dense Hopfield networks are known for their feature to prototype transition and adversarial robustness. However, previous theoretical studies have been mostly concerned with their storage capacity. We bridge this gap by studying the phase diagram of
Externí odkaz:
http://arxiv.org/abs/2401.04191
Autor:
Boukacem, Nacer Eddine, Leary, Allen, Thériault, Robin, Gottlieb, Felix, Mani, Madhav, François, Paul
Networks in machine learning offer examples of complex high-dimensional dynamical systems reminiscent of biological systems. Here, we study the learning dynamics of Generalized Hopfield networks, which permit a visualization of internal memories. The
Externí odkaz:
http://arxiv.org/abs/2312.03012