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SUMMARY:CTN: Seminar Speaker Alessandro Ingrosso
DESCRIPTION:Title:\nStatistical mechanics of transfer learning in the proportional limit\n\nAbstract:\nTransfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task\, and it crucially depends on the ability of a network to learn useful features. I will present a recent work that leverages analytical progress in the proportional regime of deep learning theory (i.e. the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio P/N finite) to develop a novel statistical mechanics formalism for TL in Bayesian neural networks. I’ll show how such single-instance Franz-Parisi formalism can yield an effective theory for TL in one-hidden-layer fully-connected neural networks. Unlike the (lazy-training) infinite-width limit\, where TL is ineffective\, in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.
URL:https://arni-institute.org/event/ctn-seminar-speaker-alessandro-ingrosso/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
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