Seminario del Dr. Matteo Negri, vincitore della procedura RTDA 2023RTDAPNRR011 02A2 FIS/02
Lunedì 10.07.2023 alle ore 11.00 in aula Convesi si terrà il seminario del Dr. Matteo Negri dal titolo:
STORAGE AND LEARNING PHASE TRANSITIONS IN THE RANDOM-FEATURES HOPFIELD MODEL
The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. Inspired by the manifold hypothesis in machine learning, we propose and investigate a generalization of the standard setting that we name "Random-Features Hopfield Model”: here, the binary pattern are (non-linear) superposition of binary feature vectors. Besides the usual retrieval phase, where the patterns can be dynamically recovered from some initial corruption, we uncover a new phase where the features hidden in the data can be recovered instead. We call this phenomena the "learning phase transition", as the features are not explicitly given to the model but rather are inferred from the patterns in an unsupervised fashion.
This could be a promising theoretical framework to understand the generalization capabilities of more complex neural networks.