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We just released a new approach for turning local search heuristics used to solve NP-hard combinatorial problems in OR into differentiable layers. The key idea is to use the neighborhoods used by these algorithms for creating MCMC proposal distributions arxiv.org/abs/2505.14240

Our paper derives momentum schedules that are functions of both the model dimension and data distribution. * On our theoretical model, this provably improves the scaling law exponents in many regimes! * And, this exponent improvement holds on LSTM experiments on C4.

Announcing Ambient Diffusion Omni — a framework that uses synthetic, low-quality, and out-of-distribution data to improve diffusion models. State-of-the-art ImageNet performance. A strong text-to-image results in just 2 days on 8 GPUs. Filtering ❌ Clever data use ✅

Pleased to see that this time, three Czech ladies are in the list of the European Research Council (ERC) Advanced Grants, and I am very proud to be among them ;). Congrats to Kateřina Čapková a Anna Durnová!

🎉 It’s official! I’ve been awarded an ERC Advanced Grant for my project on Statistical Analysis of Generative Models. More details 👉 crest.science/arnak-dalalyan… #ERCAdG European Research Council (ERC)

❓ How long does SGD take to reach the global minimum on non-convex functions? With Franck Iutzeler, J. Malick, P. Mertikopoulos, we tackle this fundamental question in our new ICML 2025 paper: "The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes"

Looking back on an inspiring day of exchange last week at Mila, where students presented cutting-edge research work to their peers during a casual poster session. See you in September for the next edition!

It's hard to plan for AGI without knowing what outcomes are even possible, let alone good. So we’re hosting a workshop! Post-AGI Civilizational Equilibria: Are there any good ones? Vancouver, July 14th Featuring: Joe Carlsmith Richard Ngo Emmett Shear 🧵

🧵(1/6) Delighted to share our ICML Conference 2025 spotlight paper: the Feynman-Kac Correctors (FKCs) in Diffusion Picture this: it’s inference time and we want to generate new samples from our diffusion model. But we don’t want to just copy the training data – we may want to sample

New paper on the generalization of Flow Matching 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**? with Quentin Bertrand, Anne Gagneux & Rémi Emonet 👇👇👇

Yet FM generates new samples! An hypothesis to explain this paradox is target stochasticity: FM targets the conditional velocity field i.e. only a stochastic approximation of the full velocity field u* *We refute this hypothesis*: very early, the approximation almost equals u*

We propose to regress directly against the optimal (deterministic) u* and show that it never degrades the performance On the opposite, removing target stochasticity helps generalizing faster.

🚀IVADO et le Centre des Compétences futures Future Skills Centre - en collaboration avec le Tech3Lab de HEC Montréal, lancent une nouvelle formation gratuite en #IA pour les professionnel(le)s du #Québec et du #Canada. Lire le communiqué de presse➡️lnkd.in/gdDkqj8N

Yes! Indeed, deep generative networks do not exactly reproduce the training set/generalize because of the inductive bias. The key difference with prev. gen. models (e.g. GANs) is the closed-form formula of FM: one can study very finely where the inductive bias comes into play!

ResNet and Neural ODEs are closely related: ResNet uses discrete residual/skip connections, while Neural ODEs generalize this to continuous transformations using ODEs. Neural ODEs *can* be seen as the limit of ResNet as the number of layers approaches infinity.

Mayavi: 3D scientific data visualization and plotting in Python docs.enthought.com/mayavi/mayavi/

Burny - Effective Omni Mathurin Massias Quentin Bertrand Yes, it is basically a different way of training normalizing flows via a regressive objective on the vector field, thus avoiding simulation step a training time. Meta uses it!

Mila's science communication contest finale showcased 6 brilliant researchers pioneering AI for medical imaging, assistive robotics, forest monitoring, inclusive urban design and more. Watch the presentations that won the hearts of the jury and the public: ow.ly/wyIY50We2p3

Slides of my talk on our ICML 2025 paper "Joint Learning of Energy-based Models and their Partition Function" mblondel.org/talks/?p=ebm.m…