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Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory. We talk about what he’s learned by studying linear deep networks and asking how depth and initial weights affect learning dynamics, when replay is appropriate (and when it’s not), how semantics develop, and what it all might tell us about deep learning in brains.
Show notes:
A few recommended texts to dive deeper:
Show notes: His new book, The Deep Learning Revolution: His Computational Neurobiology Laboratory at the Salk Institute. His faculty page at UCSD. His first...
Check out my free video series about what's missing in AI and Neuroscience Support the show to get full episodes and join the Discord...
Support the show to get full episodes and join the Discord community. David runs his lab at NYU, where they stud`y auditory cognition, speech...