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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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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...
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...
Kanaka and I discuss a few different ways she uses recurrent neural networks to understand how brains give rise to behaviors. We talk about...