Exploring Maximum Likelihood and Beyond in Neural Networks

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/HNnAwSduud

YT: https://www.youtube.com/watch?v=ZDY2nhkPZxw

We have a chat with Prof. Gary Marcus about everything which is currently top of mind for him, consciousness 

[00:00:00] Gary intro

[00:01:25] Slightly conscious

[00:24:59] Abstract, compositional models

[00:32:46] Spline theory of NNs

[00:36:17] Self driving cars / algebraic reasoning 

[00:39:43] Extrapolation

[00:44:15] Scaling laws

[00:49:50] Maximum likelihood estimation

References:

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

https://arxiv.org/abs/2201.02177

DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT

https://arxiv.org/pdf/1912.02292.pdf

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

https://arxiv.org/pdf/2002.08791.pdf