Thomas Fel

Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣

How can we interpret the algorithms and representations underlying complex behavior in deep learning models?

🌐 coginterp.github.io/neurips2025/

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How do language models generalize from information they learn in-context vs. via finetuning? In arxiv.org/abs/2505.00661 we show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. 1/

Our work finding universal concepts in vision models is accepted at #ICML2025!!!

My first major conference paper with my wonderful collaborators and friends @matthewkowal.bsky.social @thomasfel.bsky.social
@Julian_Forsyth
@csprofkgd.bsky.social

Working with y'all is the best 🥹

Preprint ⬇️!!

Accepted at #ICML2025! Check out the preprint.

HUGE shoutout to Harry (1st PhD paper, in 1st year), Julian (1st ever, done as an undergrad), Thomas and Matt!

@hthasarathan.bsky.social @thomasfel.bsky.social @matthewkowal.bsky.social

<proud advisor>
Hot off the arXiv! 🦬 "Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation" 🌍 Appa is our novel 1.5B-parameter probabilistic weather model that unifies reanalysis, filtering, and forecasting in a single framework. A thread 🧵

Have you thought that in computer memory model weights are given in terms of discrete values in any case. Thus, why not do probabilistic inference on the discrete (quantized) parameters. @trappmartin.bsky.social is presenting our work at #AABI2025 today. [1/3]

Interpreting the Linear Structure of Vision-Language Model Embedding Spaces - Kempner Institute Using sparse autoencoders, the authors show that vision-language embeddings boil down to a small, stable dictionary of single-modality concepts that snap together into cross-modal bridges. This resear...

New in the Deeper Learning blog: Kempner researchers show how VLMs speak the same semantic language across images and text.

bit.ly/KempnerVLM

by @isabelpapad.bsky.social ,Chloe Huangyuan Su, @thomasfel.bsky.social, Stephanie Gil, and @shamkakade.bsky.social

#AI #ML #VLMs #SAEs

Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable Neural firing-rate responses to sensory stimuli show progressive changes both within and across sessions, raising the question of how the brain mainta…

Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable

www.sciencedirect.com/science/arti...

Great work by Boris Sotomayor and with @battaglialab.bsky.social

PINEAPPLE, LIGHT, HAPPY, AVALANCHE, BURDEN

Some of these words are consistently remembered better than others. Why is that?
In our paper, just published in J. Exp. Psychol., we provide a simple Bayesian account and show that it explains >80% of variance in word memorability: tinyurl.com/yf3md5aj

📽️Recordings from our
@cosynemeeting.bsky.social
#COSYNE2025 workshop on “Agent-Based Models in Neuroscience: Complex Planning, Embodiment, and Beyond" are now online: neuro-agent-models.github.io
🧠🤖

[...] overall, we argue an SAE does not just reveal concepts—it determines what can be seen at all."

We propose to examine how constraints on SAE impose dual assumptions on the data, led by the amazing
@sumedh-hindupur.bsky.social 😎

New paper–accepted as *spotlight* at #ICLR2025! 🧵👇

We show a competition dynamic between several algorithms splits a toy model’s ICL abilities into four broad phases of train/test settings! This means ICL is akin to a mixture of different algorithms, not a monolithic ability.

Want strong SSL, but not the complexity of DINOv2?

CAPI: Cluster and Predict Latents Patches for Improved Masked Image Modeling.

🚨 New Paper!

Can neuroscience localizers uncover brain-like functional specializations in LLMs? 🧠🤖

Yes! We analyzed 18 LLMs and found units mirroring the brain's language, theory of mind, and multiple demand networks!

w/ @gretatuckute.bsky.social, @abosselut.bsky.social, @mschrimpf.bsky.social
🧵👇

Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More

Feng Wang, Yaodong Yu, Guoyizhe Wei, Wei Shao, Yuyin Zhou, Alan Yuille, Cihang Xie

tl;dr: we trained 1px patch size ViT so you don't have to. It improves results, but costly.

arxiv.org/abs/2502.03738

I'm delighted to share this latest research, led by the talented @hthasarathan.bsky.social
and Julian. Their work uncovered both universal conceptual across models but also unique concepts specific to DINOv2 and SigLip! 🔥

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"!

arxiv.org/abs/2502.03714

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Using mechanistic #interpretability 💻 to advance scientific #discovery 🧪 & capture striking biology? 🧬
Come see @jhartford.bsky.social's oral presentation 👨‍🏫 @ #NeurIPS2024 Interpretable AI workshop 🦾 to learn more about extracting features from large 🔬 MAEs! Paper 📄 ➡️: openreview.net/forum?id=jYl...

Did you know that @PyTorch implements the Bessel's correction to standard deviation, but not numpy or jax.

A possible source of disagreements when porting models to pytorch! @numpy_team

Picture of neural manifolds for the two choices in a decision-making task, depicted in 3D and in 2D

New preprint: "The geometry of the neural state space of decisions", work by Mauro Monsalve-Mercado, buff.ly/42wVHD5. Surprising results & predictions! (Thread) We analyze neuropixel population recordings in macaque area LIP during a reaction time, random-dot motion 1/

How do tokens evolve as they are processed by a deep Transformer?

With José A. Carrillo, @gabrielpeyre.bsky.social and @pierreablin.bsky.social, we tackle this in our new preprint: A Unified Perspective on the Dynamics of Deep Transformers arxiv.org/abs/2501.18322

ML and PDE lovers, check it out!

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders ArXiv link for AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

The research introduces AXBENCH, demonstrating that prompting outperforms complex representation methods like sparse autoencoders. It features a new weakly-supervised method, ReFT-r1, which combines interpretability with competitive performance. https://arxiv.org/abs/2501.17148

Graduate Fellowship - Kempner Institute Kempner Graduate Fellowships support a dynamic and diverse community of PhD students across a number of graduate programs at Harvard, seeding new and innovative scientific discoveries in labs across t...

The application for our #KempnerInstitute graduate fellowship for Harvard Ph.D. students is now open! Register for an upcoming virtual open house (2/6 & 2/18) and apply by 3/1. Details: bit.ly/40RY6qS

True haha !!

Haha could be, but i have another hypothesis, i'll show you next week 😉

DinoV2, C:5232... 😶‍🌫️

Does the culture you grow up in shape the way you see the world? In a new Psych Review paper, @chazfirestone.bsky.social & I tackle this centuries-old question using the Müller-Lyer illusion as a case study. Come think through one of history's mysteries with us🧵(1/13):

ICLR 2025 Workshop XAI4Science Welcome to the OpenReview homepage for ICLR 2025 Workshop XAI4Science

Join us at our upcoming workshop at ICLR, XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knwlg, Apr 27-28

submissions on a-priori (ante-hoc) & a-posteriori (post-hoc) interpretability & self-explainable models for understanding model’s behvr welcm tinyurl.com/3w8sddpm

The Mathematics of Artificial Intelligence: In this introductory and highly subjective survey, aimed at a general mathematical audience, I showcase some key theoretical concepts underlying recent advancements in machine learning. arxiv.org/abs/2501.10465