
Uri Cohen, PhD
@uricohen42
Theoretical computational neuroscience postdoc at Cambridge University (CBL lab); PhD from the Hebrew University; also @uricohen42.bsky.social
ID: 1130588521218101248
https://uricohen.github.io/ 20-05-2019 21:38:03
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1/n Iโm very excited to present this Spotlight. It was one of the more creative projects of my PhD, and also the last one with Blake Bordelon โ๏ธ๐งช๐จโ๐ป & Cengiz Pehlevan, the best coauthors you can have :) Come by this afternoon to learn "How Feature Learning Can Improve Neural Scaling Laws."





Not unplausible... xkcd.com/3085/ Am looking for Sabine Hossenfelder to weight in

Alex Nichol it represents the forward and backward pass

Elegant theoretical derivations are exclusive to physics. Right?? Wrong! In a new preprint, we: โ "Derive" a spiking recurrent network from variational principles โ Show it does amazing things like out-of-distribution generalization ๐[1/n]๐งต w/ co-lead Dekel Galor & Jake Yates
![Hadi Vafaii (@hadivafaii) on Twitter photo Elegant theoretical derivations are exclusive to physics. Right?? Wrong!
In a new preprint, we:
โ
"Derive" a spiking recurrent network from variational principles
โ
Show it does amazing things like out-of-distribution generalization
๐[1/n]๐งต
w/ co-lead <a href="/dekelgalor/">Dekel Galor</a> & Jake Yates Elegant theoretical derivations are exclusive to physics. Right?? Wrong!
In a new preprint, we:
โ
"Derive" a spiking recurrent network from variational principles
โ
Show it does amazing things like out-of-distribution generalization
๐[1/n]๐งต
w/ co-lead <a href="/dekelgalor/">Dekel Galor</a> & Jake Yates](https://pbs.twimg.com/media/GrSe1nUWYAAd6G4.jpg)


1/3 Geoffrey Hinton once said that the future depends on some graduate student being suspicious of everything he says (via Lex Fridman). He also said was that it was impossible to find biologically plausible approaches to backprop that scale well: radical.vc/geoffrey-hintoโฆ.


Data Mixing Can Induce Phase Transitions in Knowledge Acquisition ๐จ ๐ช๐ณ๐ฌ๐จ๐ต, ๐ญ๐ถ๐น๐ด๐จ๐ณ ๐ฉ๐น๐ฌ๐จ๐ฒ๐ซ๐ถ๐พ๐ต ๐ถ๐ญ ๐พ๐ฏ๐ ๐๐ถ๐ผ๐น ๐๐ฉ ๐ณ๐ณ๐ด ๐ณ๐ฌ๐จ๐น๐ต๐บ ๐ต๐ถ๐ป๐ฏ๐ฐ๐ต๐ฎ ๐ญ๐น๐ถ๐ด ๐ฏ๐ฐ๐ฎ๐ฏ-๐ธ๐ผ๐จ๐ณ๐ฐ๐ป๐ ๐ซ๐จ๐ป๐จ This paper reveals phase transitions in factual memorization


1. ืืืื ืืจืง ืขืฉื ืืช ืื ืืืื ืืืขื ืืืืืื ืืืื ืช ืืฉืจืื. ืืืืฉ ืฉืืื ืจืืฉ ืื''ื, ืจืืื''ื, ืฉืจ ืืืืืื ืืจืืฉ ืืืฉืื (ืืื ืืงืฆืื ืืืขืืืจ ืืืืชืจ ืื ืคืขื), ืื ืื ืืฉื ืืืืืื. ืืืื ืฉื ืืืืจืกืื ืขืฉื ืืช ืื ืืืื ืืืขื ืงืืืืืืช ืืืืืืขืื. ืืื ืฆืื ื-8200, ืืื ืืคืจืก ืืืืืื ืืฉืจืื ืืฆืืื ืืฉืื ืืืจืืื''ื ืืืืืข ืืคืงื ืขื


You know all those arguments that LLMs think like humans? Turns out it's not true. ๐ง In our paper "From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning" we test it by checking if LLMs form concepts the same way humans do Yann LeCun Chen Shani Dan Jurafsky



How does in-context learning emerge in attention models during gradient descent training? Sharing our new Spotlight paper ICML Conference: Training Dynamics of In-Context Learning in Linear Attention arxiv.org/abs/2501.16265 Led by Yedi Zhang with Aaditya Singh and Peter Latham



This is the syllabus of the course Geoffrey Hinton and I taught in 1998 at the Gatsby Unit (just after it was founded). Notice anything?


ืืืืื ืืช.ืื ืืงืจืื ืขื ืืืืืืืืงืืืืช, ืขื ืกืงืจื ืืช ืืืชื ื ืืืืช ืืืื ืช ืืืื ืืื ืืฉื, ืขื ืืชืืืืืช ืืืงืืืื ืืืืขืืช (ืืืื ืฉื ืคืจืืค' ืืืื ื ืืืืก ืืคืจืืค' ืืืขื ืืจืง Boaz Barak), ืืืขืืงืจ ืขื ืืจืฆืื ืืืคืขืื ืืช ืืืื ืืืืฉื ืฉื ืืื ืืื - ืจืืข ืืคื ื ืฉืื ืืื ืขืื. ืงืืฉืืจ ืืืชืื ื'ืืืจืฅ': haaretz.co.il/magazine/2025-โฆ

