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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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A few recommended texts to dive deeper:
Show notes: Sam's Computational Cognitive Neuroscience Lab.Follow Sam on Twitter: @gershbrain.The papers we discuss: What does the free energy principle tell us about the...
Support the show to get full episodes and join the Discord community. David runs his lab at UCLA where he's also a distinguished professor. ...