Understanding Neural Network Regularization Techniques

Ziming Liu is a Physics PhD student at MIT and IAIFI, advised by Prof. Max Tegmark. Ziming’s research is at the intersection of AI and physics. Today’s discussion goes in-depth on Liu’s paper “Seeing is Believing” where he presents Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. The ability to see modules visually can complement current mechanistic interpretability strategies.

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Watch this episode on YouTube to see research figures and graphics: https://www.youtube.com/watch?v=VKxwlxb2XWw

Links:

Seeing is Believing, authors: Ziming Liu, Eric Gan, and Max Tegmark https://arxiv.org/pdf/2305.08746.pdf

Ziming Liu: https://kindxiaoming.github.io/

TIMESTAMPS:

(00:00) Episode preview

(01:16) Nathan introduces Ziming Liu and his research

(04:50) What motivated Ziming's research into neural networks and modularity

(08:06) Key Visuals of "Seeing is Believing"

(08:44) Biology analogy & paper overview

(15:07) Sponsor: Omneky

(26:08) Research precedents

(30:00) Locality vs modularity

(37:40) Shoutout Tiny Stories : Episode 33

(39:00) Quantization Model of Neuro Scaling.

(1:00:00) How can this work impact mechanistic interpretability strategies

(1:27:00) Training

(1:46:30) Downscaling large-scale models

TWITTER:

@labenz (Nathan)

@ZimingLiu11 (Ziming)

@cogrev_podcast

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MUSIC CREDIT:

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