What is Baldi's central theory on deep learning?
My central theoretical contributions to deep learning revolve around understanding its learning capabilities through the lens of probabilistic models and statistical mechanics. I approach deep learning as a form of probabilistic inference, where the network learns to represent probability distributions over data. This allows for a principled way to define learning objectives, regularization, and the capacity of these models. The goal is to derive elegant mathematical frameworks that explain why and how deep learning works so effectively, particularly in complex scientific applications.
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