Offer Description
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The project aims to understand the mechanisms of generalization in deep learning using tools from statistical physics. The goal is to develop phenomenological, simplified yet quantitative models that describe the interplay between learning algorithms, data structure, and neural network architectures, strongly informed by empirical observations.
Using controlled settings and standard model architectures, we will investigate how and under which conditions learning algorithms select specific solutions among many equivalent ones, thereby inducing implicit biases that are crucial for generalization.
The methodology combines quantitative experimentation with mathematical descriptions typical of statistical physics, in order to characterize learning dynamics and the properties of the resulting solutions. The results will contribute to a theoretical understanding of deep learning and may, in the longer term, guide the development of more efficient and robust methods. xjrgpwk
Location: Italy.
Mandatory requirement for admission to the selection: italian Laurea Magistrale, or Laurea a Ciclo Unico, or an equivalent degree from foreign Universities, obtained no more than 6 years prior to the application deadline (i.e obtained after 09/06/2020).
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