Georges Le Bellier @_lebellig
Ph.D. student at CNAM on domain adaptation, generative models, and self-supervised learning for remote sensing 🛰 Previously intern @SonyCSL, @Ircam, @Inria gle-bellier.github.io Paris, France Joined October 2018-
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It was mostly a lie. I'm spending most of my social network time in the other place. I'm impressed with how fast the community moved there. My handle is lebellig (same but without the underscore)
It was mostly a lie. I'm spending most of my social network time in the other place. I'm impressed with how fast the community moved there. My handle is lebellig (same but without the underscore)
From Diffusion Models to Schrödinger Bridges - A shame not to see this NeurIPS keynote live, by the incredible @ArnaudDoucet1 - SB naturally extends flow/ bridge matching and diffusion models - Particularly useful for data to data - Links to OT neurips.cc/virtual/2024/i…
A new (and comprehensive) Flow Matching guide and codebase released! Join us tomorrow at 9:30AM @NeurIPSConf for the FM tutorial to hear more... arxiv.org/abs/2412.06264 github.com/facebookresear…
Are geospatial foundation models really impactful? 🚀🚀🌏 Check it in our new pre-print! Welcome to **PANGAEA: a global and inclusive benchmark for GFMs** arxiv.org/abs/2412.04204 a short thread 🧵
Do you work on Deep Learning for remote-sensing or Earth Observation (SSL, generative models, foundation models...) and want to move to the other place 🦋? I've initiated a pack with researchers in the field so we can find each other again! go.bsky.app/EKVwvde
I made a first scan to find people from here in the other place 🦋, maybe some of them got through: don't hesitate to ping me! (my handle is simply lebellig) The scrolling experience is so much better there...
New notebook! To get to grips with discrete flow matching paper, I've put together a little notebook to test it on MNIST. I kept the article's notations so it's easy to follow alongside the article! 🐍 notebookv1: github.com/gle-bellier/di…
I'll present Stem-JEPA this afternoon at @ISMIRConf! Hope to see you there!
I'll present Stem-JEPA this afternoon at @ISMIRConf! Hope to see you there!
I've been on twitter for a long time and really liked it and still appreciate the relevance of so many research posts on this platform. So I will not quit but start cross-posting on 🎆🛤️🏙️ where you can find me @ lebellig
What are the good practices to set up the number of epochs when using a cosine learning rate scheduler? Set it to a large number + early stopping to determine the needed epochs and then rerun the experiment?
Very nice paper! I like the decomposition between the encoder learning the deterministic components+spatial/modality alignment and the flow matching/diffusion learning the stochastic small-scale physics. (+noise scaling to avoid overfitting on the deterministic dynamics)
Very nice paper! I like the decomposition between the encoder learning the deterministic components+spatial/modality alignment and the flow matching/diffusion learning the stochastic small-scale physics. (+noise scaling to avoid overfitting on the deterministic dynamics)
Does equivariance matter at scale? Should a model rather learn equi- and invariances from data or should the architecture have the equiv property? This work provides some insights, and SCALING LAWS for each. P: arxiv.org/abs/2410.23179
New paper out! We introduce “Generator Matching” (GM), a method to build GenAI models for any data type (incl. multimodal) with any Markov process. GM unifies a range of state-of-the-art models and enables new designs of generative models. arxiv.org/abs/2410.20587 (1/5)
🚨 New paper alert! [NeurIPS 2024 spotlight] 🚨 Trajectory Flow Matching with Applications to Clinical Time Series Modeling ⏳📈 With: @yuanpu__ , @YukiKawamura_ , Andrew Loza, @Yoshua_Bengio, @dlshung, @AlexanderTong7 💻: github.com/nZhangx/Trajec… 📄: arxiv.org/abs/2410.21154 🧵👇
Maybe we just need to wait for better SSL models to have better FID with diffusion/flow matching models... (alignement model linear probing accuracy vs aligned diffusion/fm model FID) from REPA article arxiv.org/abs/2410.06940)
I had a great time collaborating with @valeriomarsocci and many others on the Pangaea project! You want to know if your earth observation encoder learned useful representations for dense tasks? Use the Pangaea benchmark! 🛰️
I had a great time collaborating with @valeriomarsocci and many others on the Pangaea project! You want to know if your earth observation encoder learned useful representations for dense tasks? Use the Pangaea benchmark! 🛰️
Sophie @leechpaxtonking
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