BI 052 Andrew Saxe: Deep Learning Theory

November 06, 2019 01:25:48
BI 052 Andrew Saxe: Deep Learning Theory
Brain Inspired
BI 052 Andrew Saxe: Deep Learning Theory

Nov 06 2019 | 01:25:48

/

Show Notes

Support the Podcast

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:

Other Episodes

Episode 0

February 20, 2022 01:25:40
Episode Cover

BI 128 Hakwan Lau: In Consciousness We Trust

Support the show to get full episodes and join the Discord community. Hakwan and I discuss many of the topics in his new book,...

Listen

Episode 0

November 12, 2020 01:26:52
Episode Cover

BI 089 Matt Smith: Drifting Cognition

Matt and I discuss how cognition and behavior drifts over the course of minutes and hours, and how global brain activity drifts with it....

Listen

Episode 0

April 01, 2023 01:31:54
Episode Cover

BI 164 Gary Lupyan: How Language Affects Thought

Support the show to get full episodes and join the Discord community. Check out my free video series about what's missing in AI and...

Listen