3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2024
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…