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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: Federico’s website.Federico’s papers we discuss: Conflicting emergences. Weak vs. strong emergence for the modelling of brain functionFrom homeostasis to behavior: balanced activity...
Show notes: Ryota founded the company Araya. Follow him on twitter: @kanair. Integrated Information Theory. Pansychism. The paper we discuss: A unified strategy for...
Support the Podcast Show notes: Raia and I discuss her work at DeepMind figuring out how to build robots using deep reinforcement learning to...