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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. I was recently invited to moderate a panel at the Annual Bernstein...
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. ...
Panelists: Yael Niv.@yael_nivKonrad [email protected] BI episodes:BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.BI 014 Konrad Kording: Regulators, Mount Up!Sam [email protected] BI episodes:BI...