My paper on Generalized Gradient Norm Clipping & Non-Euclidean (L0, L1)-Smoothness (together with collaborators from EPFL) was accepted as an oral at NeurIPS! We extend the theory for our Scion algorithm to include gradient clipping. Read about it here arxiv.org/abs/2506.01913

Our work on the generalization of Flow Matching got an oral at Neurips ! Go see @quentinbertrand.bsky.social present it there :)

🔥 Excited to announce the Workshop on the Principles of Generative Models at @euripsconf.bsky.social (the European conference parallel to NeurIPS 2025) 🇩🇰 Dec 6–7, Copenhagen 📝 Deadline for contributions: Oct 17 🔗 Website: sites.google.com/view/prigm-e...

One-day workshop on Diffusion models and Flow matching, October 24th at @ensdelyon.bsky.social Registration and call for contributions (short talk and poster) are open at gdr-iasis.cnrs.fr/reunions/mod...

Super elegant approach !

New paper on the generalization of Flow Matching www.arxiv.org/abs/2506.03719 🤯 Why does flow matching generalize? Did you know that the flow matching target you're trying to learn *can only generate training points*? w @quentinbertrand.bsky.social @annegnx.bsky.social @remiemonet.bsky.social 👇👇👇

It was received quite enthusiastically here so time to share it again!!! Our #ICLR2025 blog post on Flow Matching was published yesterday : iclr-blogposts.github.io/2025/blog/co... My PhD student @annegnx.bsky.social will present it tomorrow in ICLR, 👉poster session 4, 3 pm, #549 in Hall 3/2B 👈

The proximal operator generalizes projection in convex optimization. It converts minimisers to fixed points. It is at the core of nonsmooth splitting methods and was first introduced by Jean-Jacques Moreau in 1965. www.numdam.org/article/BSMF...

🚢🚢 deepinv v0.3.0 is here, with many new features! 🚢 🚢 Our passionate team of contributors keeps shipping more exciting tools! Deepinverse (deepinv.github.io) is a library for solving imaging inverse problems with deep learning.

I had a blast giving a summer school on generative models at AI Hub Senegal, in particular flow matching, with @quentinbertrand.bsky.social and @remiemonet.bsky.social Our material is publicly available !!! github.com/QB3/SenHubIA... ensdelyon.bsky.social

Wow, this tutorial is really a tour de force! The one I had written is super "dumbed down" as a basic intro for beginners, but to really learn the material on an intermediate/advanced level, you should check out this post...

🎓🌞 Join the PhD course on Theoretical Foundations of Machine Learning at 🇮🇹 ELLIS Unit Genoa, June 23-27, 2025, hosted by Machine Learning Genoa Center. 🗓️ Apply by: March 16, 2025 📍 University of Genoa (in-person only) 💡 More info: malga.unige.it/education/sc...

1-day workshop on Bilevel optimization and hyperparameter tuning at ENS de Lyon, on March 25th : gdr-iasis.cnrs.fr/reunions/bil... Keynote talks by @jmairal.bsky.social @tonysf.bsky.social @samuelvaiter.com, Saverio Salzo and Luce Brotcorne, contributed talks are welcome!

Our paper "PnP-Flow: Plug-and-Play Image Restoration with Flow Matching" has been accepted to ICLR 2025. Here a short explainer: We want to restore images (i.e., solve inverse problems) using pretrained velocity fields from flow matching. However, using change of variables is super costly.

Today something crazy happened. POT has reached 1000 citations (total) 🤩🚀. Very proud to be part of a scientific community that acknowledges open source research software. Please continue to use, cite and contribute to POT ! Small🧵below for those interested pythonot.github.io

MLSS coming to Senegal ! 📍 AIMS Mbour, Senegal 📅 June 23 - July 4, 2025 An international summer school to explore, collaborate, and deepen your understanding of machine learning in a unique and welcoming environment. Details: mlss-senegal.github.io

every cool thing I've ever done happened because I had one-or-two people I could constantly annoy with messages like "ooh look what I found" "ooh look what I learned" "ooh look what I broke" and who did the same in return

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition! We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423

Johnson-Lindenstrauss lemma in action: it is possible to embed any cloud of N points from R^d into R^k without distorting their respective distances too much, provided k is not too small (independently of d!) Better: any random Gaussian embedding works with high proba!

This year, there are 16 positions at CNRS in computer science (8 in "applied" domains → ask me - 8 on "fundamental" domains → ask the other David). @mathurinmassias.bsky.social has a good list of advice mathurinm.github.io/cnrs_inria_a... Official 🔗 www.ins2i.cnrs.fr/en/cnrsinfo/... Don't wait!

Conditioning of a function = ratio between highest and smallest eigenvalues of its Hessian. Higher conditioning => harder to minimize the function Gradient Descent gets faster on function with decreasing conditioning L/mu 👇

If you have a PhD and you're interested in doing a postdoc near paris on nonsmooth frank Wolfe methods for machine learning, apply for an fmjh post doc here www.fondation-hadamard.fr/fr/programme... deadline is dec 9!

New blog post: the Hutchinson trace estimator, or how to evaluate divergence/Jacobian trace cheaply. Fundamental for Continuous Normalizing Flows mathurinm.github.io/hutchinson/

Time to unearth posts from the previous network! 1°: Two equivalent views on PCA: maximize the variance of the projected data, or minimize the reconstruction error