The schedule of the LoG-Paris-Meetup is out: sites.google.com/view/learning-… !! :) Big thanks to our speakers Michael Bronstein Xavier Bresson Johannes Lutzeyer Michaël Defferrard vassilis ioannidis Dominic Masters. Registrations and poster submissions are still open ! Learning on Graphs Conference 2025 CentraleSupélec Inria

Here is the schedule ! Thanks to our amazing line of speakers for accepting to share their research and insights about Graph Machine Learning. Xavier Bresson Michael Bronstein Johannes Lutzeyer Michaël Defferrard Dominic Masters Graphcore and Vassilis N. Ioannidis from Amazon Science.

Had an incredible time hosting the inaugural Paris Learning-on-Graphs Meetup! 🎉 Thank you to everyone who attended and made it such a fantastic hybrid event! Special thanks to our inspiring speakers for their outstanding talks and to the poster presenters for sharing their work.

Layer-wise equivariance symmetries (e.g. conv layers) allow neural nets to generalise effectively. But can we learn them automatically using gradients? We show we can! Excited to share that our method ELLA has been accepted as a spotlight paper at #neurips2023. A thread.👇1/11🧵

We are happy to announce the first Learning on Graphs Conference 2024 Meetup in Lausanne, supported by VantAI! 🤗 Join us on Nov 22nd at EPFL to hear Charlotte Bunne, @ClementVignac, Dorina Thanou and Michaël Defferrard. Bring your posters! Registration: forms.gle/c4HjeuDoBTpXN9… Webpage: sites.google.com/view/log-meetu…

A lesser-known fact about ML open source contributors: About 50% of code contributors to ML paper implementations are practitioners collaborating with researchers. Here are the topk researchers & practitioners contributing to open source and open to sponsorship.

For the free online Learning on Graphs Conference 2024 we have a bunch of free local meetups! Find out if there is one close to you. Links to some of them are in the reply 👇 1/3

CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay Natasha Butt, Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺, Auke Wiggers, Corrado Rainone, David Zhang, Michaël Defferrard, Taco Cohen tl;dr: sample a program, try it, add to the replay pool. New sota on ARC arxiv.org/abs/2402.04858…

Excited to share that our paper “CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay” was accepted into ICML! Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺 Auke Wiggers Corrado Rainone David Zhang Michaël Defferrard Taco Cohen 1/5

Check👇out! New paper by Natasha Butt from AMLAB with Qualcomm collaborators on models that learn to discover programs to solve complex tasks. Congrats Natasha & Co!

1/n AI's Next Leap: Mastering Abstraction and Reasoning Imagine a world where machines can not only understand our language but also grasp the underlying logic of our thoughts, effortlessly solving problems that demand reasoning and adaptability. This is the ambitious goal of

ARC is a tough reasoning benchmark where modern LLMs far underperform humans still. Great to see that there's serious additional backing! Coincidentally, we just open-sourced CodeIt, our LLM-improvement approach for ARC: github.com/Qualcomm-AI-re…

…and the ChebNet guy ⁦Michaël Defferrard⁩

Switzerland now requires all government software to be open source zdnet.com/article/switze…

Come see our poster #715 on CodeIt today at #ICML2024 13.30-15.00 Halle C. We approach ARC by self-improving LLMs with prioritized hindsight replay. Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺 Auke Wiggers Corrado Rainone David Zhang Michaël Defferrard Taco Cohen

Best part of ICML so far! Fun dinner with lots of nice people working on graph machine learning and beyond.

Exploring “dark-matter” protein folds using deep learning Cell Systems • Introducing Genesis VAE, a convolutional variational autoencoder that transforms low-resolution protein fold sketches into designable, stable 3D models. • Genesis VAE enables rapid exploration of

I'll make the talk fun, and insightful. IMHO, we had a breakthrough moment with the CARTE paper openreview.net/forum?id=9kArQ…

Looking at the thread. The common frame to look at the more general phenomenon involves an eigenproblem of the form Oƒ = λƒ, where the operator O encodes either: a symmetry (translations, rotations, general group transformations), or a a statistic (e.g. covariance, correlation),

Caring for your home is a lesson in the 2nd law: Cease working against entropy and nature decays it into a forest.