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

May 30, 2019 01:11:08
Episode Cover

BI 036 Roshan Cools: Cognitive Control and Dopamine

Show notes: Roshan will deliver a keynote address at the upcoming CCN conference.Roshan's Motivational and Cognitive Control lab.Follow her on Twitter: @CoolsControl.Her TED Talk...

Listen

Episode

February 19, 2019 01:14:09
Episode Cover

BI 028 Sam Gershman: Free Energy Principle & Human Machines

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...

Listen

Episode 0

March 12, 2021 01:25:00
Episode Cover

BI 100.2 Special: What Are the Biggest Challenges and Disagreements?

In this 2nd special 100th episode installment, many previous guests answer the question: What is currently the most important disagreement or challenge in neuroscience...

Listen