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lebellig

@lebellig.bsky.social
2.4K followers 704 following 181 posts

Postdoc @INRIA, Ockham team, on generative models. Previously intern @SonyCSL, @Ircam, @INRIA 🌎 Personal website: lebellig.github.io

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lebellig @lebellig.bsky.social · 29/09/2026
Looking for insights into the performance gap between unconditional and conditional diffusion models when the guidance weight is set to 0 👀
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lebellig @lebellig.bsky.social · 23/09/2026
New interest: super-resolved images from undertrained flow matching models
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lebellig @lebellig.bsky.social · 23/09/2026
I should keep this checkpoint for later
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lebellig @lebellig.bsky.social · 10/09/2026
So cool that we have mentats to solve all the difficult math problems we can finally spend our free time fighting across sand deserts under a blazing sun.
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ELLIS @ellis.eu · 09/09/2026
Meet Charlotte Pelletier, Assoc. Prof at Université Bretagne Sud 🇫🇷 & ELLIS Member. She researches AI, particularly in the scope of time series analysis with applications in remote sensing and Earth observation. Her advice for young scientists focuses on investing in a strong professional network.
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Lucas Degeorge @lucasdegeorge.bsky.social · 03/09/2026
🚀 New paper: Balancing Frequencies and Pixels in Flow Matching We tackle the low-frequency bias in pixel-space flow matching and train JiT up to 40% faster without any architectural changes. 📄 Read it here: arxiv.org/abs/2609.02748
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lebellig @lebellig.bsky.social · 02/09/2026
We’re excited to release the updated article and code of InSARFlow! 🌊⛵ InSARFlow is a Riemannian flow matching model designed to denoise SAR interferograms while preserving the cyclical nature of phase differences.🌀 📄 Article: hal.science/hal-05710871... 💻 Code: github.com/lebellig/ins...
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Al Merose @al.merose.com · 21/08/2026
My lab is looking for an Assistant Research Scientist to work on AI for Climate Science. No Ph.D is required for this position, just a BS and relevant experience. The application deadline is Sept 20. Join me in making better climate models! apply.interfolio.com/191796
apply.interfolio.com
Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio
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lebellig @lebellig.bsky.social · 19/08/2026
Yesterday I watched a movie about an AI4Science researcher who gets angry after having his research grant application rejected (badly explained movie plot). Maybe we should take it as a warning about research funding cuts
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NeurIPS Europe @neuripseurope.bsky.social · 18/08/2026
We are happy to announce that 28 workshops have been accepted for the Paris event, as part of the 102 accepted NeurIPS workshops: blog.neurips.cc/2026/08/10/a... They will take place on Sat Dec 12 + Sun Dec 13, 2026 (for Paris) The suggested deadline for Workshop submissions is close (Aug 29th)!
blog.neurips.cc
Announcing the NeurIPS 2026 Workshops – NeurIPS Blog
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Climate AI Nordics @climateainordics.com · 06/08/2026
🌱 Big news from Climate AI Nordics (CAIN)! We are officially registered as an NGO! 🌍✨ Network members can now become official members with AGM voting rights to help shape our future. Join us or get involved! More info: climateainordics.com/news/2026-08-04-cain-ngo/ 🤖💚
climateainordics.com
Climate AI Nordics is now an official NGO
Climate AI Nordics becomes an official NGO (ideell förening)
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Al Merose @al.merose.com · 30/07/2026
Here's the tale of how @jder.bsky.social and I scaled Samudra, a neural ocean emulator capable of predicting 8 years of the ocean on a single GPU, to operate at a full 1/4° resolution (16x the size in bytes). It was quite a humbling process.
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lebellig @lebellig.bsky.social · 24/07/2026
NeurIPS submissions confirmed to be a heat-loving species. Warmer year, bigger bloom. Every degree we add, the deadline gets denser 🌻 Good news for the field, we're having a really good growing season 👨‍🌾
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Nicolas Audebert @nshaud.bsky.social · 21/07/2026
You can train your image-to-image flow matching model on badly aligned data, you just have to tell it how bad it is. 🫣 Great work from @lebellig.bsky.social and Aimi Okabayashi with cool applications to remote sensing. It's FlowEO 2.0!
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lebellig @lebellig.bsky.social · 21/07/2026
The last project of my PhD is finally out! 🪴 It was a pleasure collaborating with Aimi on this work! We introduce A²BM: Alignment-Aware Bridge Matching, a new framework for image-to-image translation with weakly aligned image pairs. Paper 📄: arxiv.org/pdf/2607.16294
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Mathurin Massias @mathurinmassias.bsky.social · 21/07/2026
I organize a 1 day workshop on Generative modelling @ENS Lyon, October 9th Call for oral/poster contributions is open; details at gdr-iasis.cnrs.fr/reunions/mod...
gdr-iasis.cnrs.fr
Modèles génératifs : diffusion, flow matching - GdR IASIS
Les demandes de prise en charge de missions par le GdR IASIS doivent parvenir à la gestionnaire du GdR avant le 25 septembre. Les modèles génératifs ont connu de récentes avancées spectaculaires, au p...
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Al Merose @al.merose.com · 08/07/2026
I'd like to announce that at @openathena.ai, @jder.bsky.social and I helped @m2lines.bsky.social release Samudra 2. We scaled this neural ocean emulator to train on 16x the size of data in bytes on the same hardware budget. We can now skillfully predict 8 years of the ocean on a single GPU at a 1/4°
medium.com
🌊 Samudra 2: A Fast, Cheap AI Ocean Model, Now at the Scale That Matters
M²LInES’ neural ocean emulator now runs multi-year simulations at eddy-permitting resolution on a single GPU, turning a supercomputer-scale…
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Mathurin Massias @mathurinmassias.bsky.social · 07/07/2026
Slides for our ICML tutorial on Memorization and Generalization of Diffusion and Flow Matching Models are now available ! 🌀 memorization-generalization.github.io @quentinbertrand.bsky.social
memorization-generalization.github.io
ICML 2026 Tutorial - Generalization and Memorization in Flow Matching and Diffusion
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lebellig @lebellig.bsky.social · 03/07/2026
Heading to #ICML2026 🇰🇷 and interested in diffusion models, flow matching, and their generalization capabilities? Don't miss the excellent tutorial by @mathurinmassias.bsky.social and @quentinbertrand.bsky.social on Monday! 📍 Hall D1 🗓️ Monday, July 6 🕘 9:00–11:30 AM Details: icml.cc/virtual/2026...
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lebellig @lebellig.bsky.social · 03/07/2026
Heading to #ICML2026 🇰🇷 and interested in diffusion models, flow matching, and their generalization capabilities? Don't miss the excellent tutorial by @mathurinmassias.bsky.social and @quentinbertrand.bsky.social on Monday! 📍 Hall D1 🗓️ Monday, July 6 🕘 9:00–11:30 AM Details: icml.cc/virtual/2026...
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Guillaume Astruc @gastruc.bsky.social · 23/06/2026
🛰️ Introducing UniverSat: one transformer backbone for Earth Observation that handles ANY sensor, ANY spatial, spectral & temporal resolution, ANY scale — with a single set of weights. 🌍
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lebellig @lebellig.bsky.social · 22/06/2026
I took Lyon's rainy days in early May as a warm welcome, now with the heatwave I think my acclimatisation is complete, so it's time to make it official! I've started a postdoc on generative models in Inria's Ockham team working with @mathurinmassias.bsky.social and @quentinbertrand.bsky.social ☀️
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Nicolas Dufour @nicolasdufour.bsky.social · 19/06/2026
We explored the impact of variability sources in generative modeling. Turns out, we've been neglecting the error bars associated with training variability all along! We should aim to report results that we are sure of their scientific validity, instead of seed engineering!
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Coleen Murphy @ctmurphy1.bsky.social · 17/06/2026
one anecdote: I was searching for a link to one of my old papers on Google, and the automated Gemini summary attributed all of my lab's work to my husband
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Gabriel Peyré @gabrielpeyre.bsky.social · 16/06/2026
The alpha version of my new book "Optimal Transport for Machine Learners" is out, with in particular an online version with interactive figures www.gpeyre.com/ot4ml/
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David Picard @davidpicard.eurosky.social · 28/05/2026
🎆 New paper! "Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields", by Julien Lalanne, accepted to ICML'26 🥳 We're proposing flow-matching for inpainting in ultra-sparse setup, with applications to seismic interpolation. 📜 arxiv.org/abs/2605.28625 1/
arxiv.org
Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussian Processes (GP) for uncertainty-aware predicti...
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Gilles Louppe @glouppe.bsky.social · 22/05/2026
"Accept (spotlight)" at ICML'26 😎 Our paper brings particle filters back to life: autoregressive diffusion models + posterior sampling yield optimal proposals for Bayesian filtering, scaling up to GenCast-sized systems. arxiv.org/abs/2605.20028 w/ Thomas Savary and @francois-rozet.bsky.social
arxiv.org
Training-Free Bayesian Filtering with Generative Emulators
Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical system from observations. Among existing approaches to solve this problem, particle filters are theoret...
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Nicolas Dufour @nicolasdufour.bsky.social · 20/05/2026
Thrilled to share that MIRO is accepted to ICML 2026 @icmlconf.bsky.social ! 🎉 By training on the reward scores, we can simply condition the model on high rewards at inference time to guarantee top-tier, aligned outputs. We’ve updated our paper with some additional results!
arxiv.org
MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one reward model to align the generator to the reward, typi...
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Nicolas Audebert @nshaud.bsky.social · 20/05/2026
📢 The TerraBytes workshop is returning for a 2nd edition - this time at ECCV 2026. Submit your paper before June 18th and join in Malmö, Sweden! 🔗 terrabytes-workshop.github.io
TerraBytes II call for papers announcement
"Towards global datasets and models for Earth Observation"
With organizers from ESA Phi-lab, LASTIG, Le Cnam, KTH, Universidad de Chile, and asterisk labs
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David Picard @davidpicard.eurosky.social · 15/05/2026
👏 Folks! If you are curious about the Generative Modeling via Drifting paper, but you find it difficult to understand → I wrote a different interpretation of it. It's called: "An Expectation-Maximization interpretation of Generative Modeling via Drifting" davidpicard.github.io/pdf/An_Expec...
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lebellig @lebellig.bsky.social · 28/04/2026
My PhD thesis manuscript will be available in the coming months, but I’ve written two blog posts based on the related work chapter: 1. Generative modeling with flow-based models 🪚 2. Data-translation with flow and diffusion bridges 🔨 Open to feedback and discussions! lebellig.github.io/blog/
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Al Merose @al.merose.com · 07/05/2026
Want better local models? Want models that are made completely transparently? OpenAthena.ai is hiring on our Marin.community team. We’re especially looking for SWEs and research scientists with experience in data and post training. DM me.
openathena.ai
Open Athena
Open Athena is a nonprofit that accelerates academia with capabilities from the AI frontier
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Sander Dieleman @sedielem.bsky.social · 06/05/2026
My first blog post in over a year is a deep dive on flow maps🗺️, or how to learn the integral of a diffusion model to enable faster sampling and several other cool tricks. It's the longest one yet👀 Let me know what you think! sander.ai/2026/05/06/f...
sander.ai
Learning the integral of a diffusion model
A deep dive on flow maps.
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lebellig @lebellig.bsky.social · 28/04/2026
My PhD thesis manuscript will be available in the coming months, but I’ve written two blog posts based on the related work chapter: 1. Generative modeling with flow-based models 🪚 2. Data-translation with flow and diffusion bridges 🔨 Open to feedback and discussions! lebellig.github.io/blog/
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lebellig @lebellig.bsky.social · 28/04/2026
Delighted to have successfully defended my PhD thesis on "Generative models for Earth Observation, from denoising to domain adaptation" 🪴 This wouldn’t have been possible without the support of my colleagues, my PhD advisor, and my family and friends. Thank you all!
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Gabriel Peyré @gabrielpeyre.bsky.social · 16/04/2026
While it is intuitively clear that straighter trajectories should reduce discretization error when integrating an ODE (for instance, in flow matching), I could not find a precise bound. I therefore rewrote the proof of Cauchy-Lipschitz to make this explicit. github.com/gpeyre/Discr...
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Gabriel Peyré @gabrielpeyre.bsky.social · 09/04/2026
I wrote a short mathematical companion tutorial to my notebook on discrete diffusion models. It gives an informal derivation of the connection between maximum likelihood estimation of the backward transition kernel and denoising score matching. github.com/gpeyre/Discr...
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Marta Skreta @martaowesyou.bsky.social · 08/04/2026
What if AI could invent enzymes that nature hasn’t seen? 👩‍🔬🧑‍🔬 Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design 📝 Blog: disco-design.github.io 📄 Paper: arxiv.org/abs/2604.05181 💻 Code: github.com/DISCO-design...
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lebellig @lebellig.bsky.social · 08/04/2026
Two research blog posts that look really interesting 👀 "Teaching AI to Invent Enzymes Nature Never Imagined", DISCO: Diffusion for Sequence-structure CO-design disco-design.github.io "How to Generate Text in One Step", Flow Map Language Models one-step-lm.github.io/blog/
disco-design.github.io
DISCO — Teaching AI to Invent Enzymes Nature Never Imagined
DISCO is a multimodal generative model that co-designs protein sequence and 3D structure to create entirely new enzymes for reactions never seen in biology.
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David Picard @davidpicard.eurosky.social · 08/04/2026
🚨 arxiv.org/abs/2604.06129 PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer This paper is the result of doing a lab-wide hackathon on an idea I've had for some time. Probably the paper with the highest number of authors I've ever done. It's a CVPR Findings 26. Thread 🧵👇
arxiv.org
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a comp...
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Gabriel Peyré @gabrielpeyre.bsky.social · 07/04/2026
For those interested in normalized gradient methods and optimal transport: I introduce a new class of "spectral" Wasserstein distances for which spectrally normalized gradient descent (Muon but without momentum and small step size ...) is a spectral-W gradient flow: arxiv.org/abs/2604.04891
arxiv.org
Muon Dynamics as a Spectral Wasserstein Flow
Gradient normalization is central in deep-learning optimization because it stabilizes training and reduces sensitivity to scale. For deep architectures, parameters are naturally grouped into matrices ...
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Climate AI Nordics @climateainordics.com · 27/03/2026
We're excited to provide more information about our upcoming annual workshop - The 2026 Nordic Workshop on AI for Climate (climateainordics.com/events/2026-...) - to be held on June 26th, 2026 at University of Copenhagen! Registration link coming very soon!
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Olga Zaghen @olgatticus.bsky.social · 23/03/2026
🔮 Working on ML on curved manifolds? Don't miss out on Jacobi Fields! 🔮 I wrote a quick, highly visual and hopefully accessible introduction to the topic: "Jacobi Fields in Machine Learning" 🤠 Check it out here: olgatticus.github.io/blog/jacobi-...!
olgatticus.github.io
Jacobi Fields in Machine Learning — Olga Zaghen
An intuitive introduction to Jacobi fields and their applications in machine learning on Riemannian manifolds.
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NeurIPS Europe @neuripseurope.bsky.social · 23/03/2026
Today NeurIPS is announcing our official satellite event in Paris. After responding to the call from Ellis following the success of EurIPS in December, we are pleased to reach a new milestone by joining forces with the NeurIPS organizing committee for the 2026 edition.
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lebellig @lebellig.bsky.social · 18/03/2026
Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis by Hila Chefer et al. (arxiv.org/abs/2603.06507). New SSL loss for flow matching that encourages meaningful representation learning without relying on an external visual encoder for alignment. Improves generation on many modalities.
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Kieran Didi @kdidi.bsky.social · 17/03/2026
📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n
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Sander Dieleman @sedielem.bsky.social · 16/03/2026
In October, I gave a talk at ML in PL in Warsaw: a whirlwind tour of what goes into training image and video generation models at scale. 📺 video: www.youtube.com/watch?v=qFIT... 🖼️ slides: docs.google.com/presentation...
youtube.com
Sander Dieleman - Diffusion models for image and video generation | ML in PL 2025
YouTube video by ML in PL
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Nicolas Audebert @nshaud.bsky.social · 13/03/2026
📢 Je recrute : ingé ou postdoc (12 mois) ➡️ www.ign.fr/nous-rejoind... Venez entraîner des grands modèles génératifs pour le bien commun : 🗺️ données ouvertes (images aériennes/satellites) 🏞️ application au suivi du changement climatique et à la gestion des catastrophes naturelles #lastig #ign
ign.fr
INGÉNIEUR·E DE RECHERCHE : MODÈLES GÉNÉRATIFS PROFONDS POUR L'IMAGERIE SATELLITAIRE - CDD 12 MOIS
Famille: Recherche et Enseignement Type de contrat : CDD lié à convention Catégorie: A Lieu de travail : Champs-sur-Marne Réf. 20260313-1590
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lebellig @lebellig.bsky.social · 11/03/2026
The Spacetime of diffusion models: an information geometry perspective by Rafał Karczewski et al. (arxiv.org/abs/2505.17517) blog: rafalkarczewski.github.io/blog/2026/di... Geodesics in the (xt, t) spacetime of diffusion models -> new distance between clean data points + transition path sampling!!
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Gabriel Peyré @gabrielpeyre.bsky.social · 01/03/2026
I have added a new tutorial on discrete diffusion models: github.com/gpeyre/ot4ml
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