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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.
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Support the show to get full episodes and join the Discord community. As some of you know, I recently got back into the research...
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Steve and I discuss his long and productive career as a theoretical neuroscientist. We cover his tried and true method of taking a large...