8 citations · 18 across the 5 of their papers we have counts for
5 papers
Explaining Machine Learning and Memorization with Statistical Mechanics
Robin Theriault
Artificial neural networks (NNs) and machine learning (ML) algorithms are poorly understood from a theoretical perspective, which makes it difficult to fully realize their potentia…
Saddle Hierarchy in Dense Associative Memory
Robin Thériault, Daniele Tantari
Dense Associative Memory (DAM) models have been attracting renewed attention since they were shown to be robust to adversarial examples and closely related to cutting edge machine…
Modeling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Setting
Robin Thériault, Francesco Tosello, Daniele Tantari
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 st…
Dense Hopfield Networks in the Teacher-Student Setting
Robin Thériault, Daniele Tantari
Dense Hopfield networks are known for their feature to prototype transition and adversarial robustness. However, previous theoretical studies have been mostly concerned with their…
A Waddington landscape for prototype learning in generalized Hopfield networks
Nacer Eddine Boukacem, Allen Leary, Robin Thériault +3
Networks in machine learning offer examples of complex high-dimensional dynamical systems reminiscent of biological systems. Here, we study the learning dynamics of Generalized Hop…