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New insights into training dynamics of deep classifiers
MIT Center for Brains, Minds and Machines researchers provide one of the first theoretical analyses covering optimization, generalization, and approximation in deep networks and offers new insights into the properties that emerge during training.
Tomaso poggio
Akshay rangamani
Department of brain
Mcgovern institute
Cognitive sciences
Brown university
Deep classifiers
Square loss
Slow rank
Neural collapse
Generalization bounds
Govern institute
Eugene mcdermott professor
Mit center for brains minds and machines
Tomaso poggio
Omer galanti
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