Martina Vilas

Eureka Inference-Time Scaling Insights: Where We Stand and What Lies Ahead - Microsoft Research Understanding and measuring the potential of inference-time scaling for reasoning. The new Eureka study tests nine state-of-the-art models on eight diverse reasoning tasks.

All Eureka inference-time scaling insights are now available here: www.microsoft.com/en-us/resear... It was fun sharing these and more together with Vidhisha Balachandran @vidhishab.bsky.social and Vibhav Vineet at #ICLR2025.

Looking forward to presenting this work next week at #ICLR2025! DM me if you are attending and want to grab a coffee to discuss these topics 💫

December 5th our ML theory group at Cohere For AI is hosting @mathildepapillon.bsky.social to discuss their recent review arxiv.org/abs/2407.09468 on geometric/topological/algebraic ML.

Join us online 💫

I’m putting together a starter pack for researchers working on human-centered AI evaluation. Reply or DM me if you’d like to be added, or if you have suggestions! Thank you!

(It looks NLP-centric at the moment, but that’s due to the current limits of my own knowledge 🙈)

go.bsky.app/G3w9LpE

I tried to find everyone who works in the area but I certainly missed some folks so please lmk...
go.bsky.app/BYkRryU

Does anyone know of any feeds (or similar) for student internship opportunities in ML/CV/NLP?

I've found starter packs on NLP, vision, graphics, etc. But personally, I would love to know and hear from researchers working on vision-language. So, let me know if you'd like to join this starter pack, would be happy to add!

go.bsky.app/TENRRBb

How do LLMs learn to reason from data? Are they ~retrieving the answers from parametric knowledge🦜? In our new preprint, we look at the pretraining data and find evidence against this:

Procedural knowledge in pretraining drives LLM reasoning ⚙️🔢

🧵⬇️

LLMs tend to match problem-solving strategies based on textual similarity rather than truly understanding the underlying principles of mathematical problems.

Paper: Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology

A starter pack of people working on interpretability / explainability of all kinds, using theoretical and/or empirical approaches.

Reply or DM if you want to be added, and help me reach others!

go.bsky.app/DZv6TSS

If you’re interested in mechanistic interpretability, I just found this starter pack and wanted to boost it (thanks for creating it @butanium.bsky.social !). Excited to have a mech interp community on bluesky 🎉

go.bsky.app/LisK3CP

👋 I also work on the field (examples on my profile). Would love to be added!

I forgot from whom in my feed I got this from, but anyway, this network analyzer is crazy efficient. It gives you ideas for accounts to follow based on your own followees. I just added 50 accounts or so.

bsky-follow-finder.theo.io

there are many smart speakers and thinkers around AI/ML and/or NLP. but i find almost everything to be kinda predictable by now, minor stylistic variations on the same story. who are some *interesting* speakers i should listen/read? i want things that may surprise or inspire me.

Any Latin Americans here working in Cognitive Science, very broadly construed? (Neuroscience, Psychology, Artificial Intelligence, Anthropology, Linguistics, Economics, Ethics, Philosophy, and more…)

I thought I’d create a starter pack but I could only find a handful of us. Say hi?

It is intuitive to observe some complex-looking model behavior (e.g., the classification of images of different animals using an abstract category) and infer an interesting capacity of the model (e.g., the ability to build rich representations that abstract away from particular animals).

We found that the mechanisms behind the emergence of these representations are similar to those of LLMs, and can be found across a variety of vision transformers and layer types.

Analyzing Vision Transformers for Image Classification in Class Embedding Space Despite the growing use of transformer models in computer vision, a mechanistic understanding of these networks is still needed. This work introduces a method to reverse-engineer Vision Transformers t...

[2/2] In our #NeurIPS2023 paper, we introduce a simple and efficient approach to investigate how class prototype representations emerge in vision transformers trained for image classification.

arxiv.org/abs/2310.18969

Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debated. Moreover, recent...

We show how many of the issues in the AI Inner Interpretability field are similar to those in Cognitive Neuroscience.

We thus argue that we can adapt conceptual and methodological frameworks from CogNeuro to make progress in interpretability research.

arxiv.org/abs/2406.01352

[1/2] Position paper at #ICML2024 “An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience"

Hi BlueSky! 🦋 I’m a computer science PhD student with a background in cognitive neuroscience. Working at the intersection of these topics, my research focuses on reverse engineer the cognitive capacities of AI models 🧠💻

Some recent examples 👇

I made a starter pack with the people doing something related to Neurosymbolic AI that I could find.

Let me know if I missed you!
go.bsky.app/RMJ8q3i

New here? Interested in AI/ML? Check out these great starter packs!

AI: go.bsky.app/SipA7it
RL: go.bsky.app/3WPHcHg
Women in AI: go.bsky.app/LaGDpqg
NLP: go.bsky.app/SngwGeS
AI and news: go.bsky.app/5sFqVNS

You can also search all starter packs here: blueskydirectory.com/starter-pack...