lebellig
@lebellig.bsky.social
Ph.D. student on generative models and domain adaptation for Earth observation 🛰
Previously intern @SonyCSL, @Ircam, @Inria
🌎 Personal website: https://lebellig.github.io/
Previously intern @SonyCSL, @Ircam, @Inria
🌎 Personal website: https://lebellig.github.io/
Pinned
lebellig
@lebellig.bsky.social
· Nov 18
GitHub - gle-bellier/flow-matching: Annotated Flow Matching paper
Annotated Flow Matching paper. Contribute to gle-bellier/flow-matching development by creating an account on GitHub.
github.com
I created 3 introductory notebooks on Flow Matching models to help get started with this exciting topic! ✨
1. Annotated Flow Matching paper: github.com/gle-bellier/...
2. Discrete Flow Matching: github.com/gle-bellier/...
3. Minimal FM in Jax: github.com/gle-bellier/...
1. Annotated Flow Matching paper: github.com/gle-bellier/...
2. Discrete Flow Matching: github.com/gle-bellier/...
3. Minimal FM in Jax: github.com/gle-bellier/...
My first impression is that it will look like GANs for inverse problems but maybe there is something to do with the training drift term
February 8, 2026 at 2:25 PM
My first impression is that it will look like GANs for inverse problems but maybe there is something to do with the training drift term
imo the original paper (arxiv.org/abs/2602.04770) is well written, but there are already some implementations/blog posts about it (github.com/Algomancer/M...)
Generative Modeling via Drifting
Generative modeling can be formulated as learning a mapping f such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iteratively at inference...
arxiv.org
February 7, 2026 at 5:22 PM
imo the original paper (arxiv.org/abs/2602.04770) is well written, but there are already some implementations/blog posts about it (github.com/Algomancer/M...)
🔳 Discrete drifting models
🔳 Riemannian drifting models
🔳 Optimal Transport drifting models
🔳 Image2image drifting models
🔳 Time-dependent drifting models (tricky one)
🔳 Adversarial drifting models
🔳 Wasserstein drifting models
🔳 Variational drifting models
🔳 Functional drifting models
🔳 Riemannian drifting models
🔳 Optimal Transport drifting models
🔳 Image2image drifting models
🔳 Time-dependent drifting models (tricky one)
🔳 Adversarial drifting models
🔳 Wasserstein drifting models
🔳 Variational drifting models
🔳 Functional drifting models
February 7, 2026 at 4:49 PM
🔳 Discrete drifting models
🔳 Riemannian drifting models
🔳 Optimal Transport drifting models
🔳 Image2image drifting models
🔳 Time-dependent drifting models (tricky one)
🔳 Adversarial drifting models
🔳 Wasserstein drifting models
🔳 Variational drifting models
🔳 Functional drifting models
🔳 Riemannian drifting models
🔳 Optimal Transport drifting models
🔳 Image2image drifting models
🔳 Time-dependent drifting models (tricky one)
🔳 Adversarial drifting models
🔳 Wasserstein drifting models
🔳 Variational drifting models
🔳 Functional drifting models
Very cool PhD project on generative models for dense detection of rare events in Earth Observation 🌍🌱
Nicolas has been my supervisor for the last 3 years, highly recommend doing a PhD with him!
Nicolas has been my supervisor for the last 3 years, highly recommend doing a PhD with him!
📢 Fully funded PhD - 🌍 Dense Detection of Rare Events in Remote Sensing using Generative Models
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
26-252 Dense Detection of Rare Events in Remote Sensing Using Generative Models
Offre d’emploi 26-252 Dense Detection of Rare Events in Remote Sensing Using Generative Models au CNES à 75003 Paris !
recrutement.cnes.fr
February 3, 2026 at 2:08 PM
Very cool PhD project on generative models for dense detection of rare events in Earth Observation 🌍🌱
Nicolas has been my supervisor for the last 3 years, highly recommend doing a PhD with him!
Nicolas has been my supervisor for the last 3 years, highly recommend doing a PhD with him!
Reposted by lebellig
📢 Fully funded PhD - 🌍 Dense Detection of Rare Events in Remote Sensing using Generative Models
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
26-252 Dense Detection of Rare Events in Remote Sensing Using Generative Models
Offre d’emploi 26-252 Dense Detection of Rare Events in Remote Sensing Using Generative Models au CNES à 75003 Paris !
recrutement.cnes.fr
February 3, 2026 at 12:51 PM
📢 Fully funded PhD - 🌍 Dense Detection of Rare Events in Remote Sensing using Generative Models
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
Leverage generative models, unsupervised segmentation and explainability techniques to map disasters
w/ @javi-castillo.bsky.social and Flora Weissgerber
Apply ⤵️
recrutement.cnes.fr/fr/annonce/4...
Is it a vscode plugin?
January 23, 2026 at 10:25 AM
Is it a vscode plugin?
Meta Flow Maps enable scalable reward alignment, Peter Potaptchik et al. (arxiv.org/abs/2601.14430)
This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.
This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.
January 22, 2026 at 9:15 AM
Meta Flow Maps enable scalable reward alignment, Peter Potaptchik et al. (arxiv.org/abs/2601.14430)
This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.
This article introduces Meta Flow Maps: a stochastic generalization of consistency models (one-step generation) that allows efficient reward steering at inference time or during fine-tuning.
Reposted by lebellig
I'm excited to open the new year by sharing a new perspective paper.
I give a informal outline of MD and how it can interact with Generative AI. Then, I discuss how far the field has come since the seminal contributions, such as Boltzmann Generators, and what is still missing
I give a informal outline of MD and how it can interact with Generative AI. Then, I discuss how far the field has come since the seminal contributions, such as Boltzmann Generators, and what is still missing
January 16, 2026 at 10:25 AM
I'm excited to open the new year by sharing a new perspective paper.
I give a informal outline of MD and how it can interact with Generative AI. Then, I discuss how far the field has come since the seminal contributions, such as Boltzmann Generators, and what is still missing
I give a informal outline of MD and how it can interact with Generative AI. Then, I discuss how far the field has come since the seminal contributions, such as Boltzmann Generators, and what is still missing
Should we ban Brian Eno from bandcamp?
I applaud BandCamp for promoting human creativity, yet there's nuance needed since tech has been used creatively by musicians for ages. Are algorithmic compositions generative AI? ML amp models? LANDR mastering?
Will retroactive enforcement violate agreements w/ users?
stereogum.com/2485199/band...
Will retroactive enforcement violate agreements w/ users?
stereogum.com/2485199/band...
Bandcamp Bans AI Music
AI music has become a big problem on streaming services. Remember the AI-generated psych-rock band the Velvet Sundown and the AI-generated metalcore band Broken Avenue racking up streams on Spotify? R...
stereogum.com
January 15, 2026 at 5:08 PM
Should we ban Brian Eno from bandcamp?
Reposted by lebellig
New blog 💙: I reflect on why I worked on what I worked on...
I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting.
chaitjo.substack.com/p/phd-thesis...
I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting.
chaitjo.substack.com/p/phd-thesis...
A Cambridge PhD thesis in three research questions
Geometric Deep Learning for Molecular Modelling and Design: A personal scientific journey
chaitjo.substack.com
January 8, 2026 at 4:38 AM
New blog 💙: I reflect on why I worked on what I worked on...
I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting.
chaitjo.substack.com/p/phd-thesis...
I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting.
chaitjo.substack.com/p/phd-thesis...
Yes estimating distance between distributions with single sample sounds irrelevant. I wonder if flow-based artefacts are sufficiently similar across models with the same FID, allowing us to learn the score predictive model. I may try later!
January 8, 2026 at 5:56 PM
Yes estimating distance between distributions with single sample sounds irrelevant. I wonder if flow-based artefacts are sufficiently similar across models with the same FID, allowing us to learn the score predictive model. I may try later!
Agree! I wonder if some generation artefacts are signatures that allow to predict the FID score (suppose that they are present in almost all generated images by a given model)
January 8, 2026 at 5:41 PM
Agree! I wonder if some generation artefacts are signatures that allow to predict the FID score (suppose that they are present in almost all generated images by a given model)
You may add the real test (or training 👀) dataset if you are into leaderboard chasing
1. Select many diffusion/flow-matching models
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
January 8, 2026 at 5:33 PM
You may add the real test (or training 👀) dataset if you are into leaderboard chasing
1. Select many diffusion/flow-matching models
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
January 8, 2026 at 3:17 PM
1. Select many diffusion/flow-matching models
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
2. Generate 50k images per model
3. Use FID of each set as a label
4. Train a model to predict FID from a single image
What’s the probability this actually works, gives a cheap proxy for FID and enable fast generative model prototyping?
Reposted by lebellig
We introduce epiplexity, a new measure of information that provides a foundation for how to select, generate, or transform data for learning systems. We have been working on this for almost 2 years, and I cannot contain my excitement! arxiv.org/abs/2601.03220 1/7
January 7, 2026 at 5:28 PM
We introduce epiplexity, a new measure of information that provides a foundation for how to select, generate, or transform data for learning systems. We have been working on this for almost 2 years, and I cannot contain my excitement! arxiv.org/abs/2601.03220 1/7
Reposted by lebellig
📖 We put together with Mike Davies a review of self-supervised learning for inverse problems, covering the main approaches in the literature with a unified notation and analysis.
arxiv.org/abs/2601.03244
arxiv.org/abs/2601.03244
Self-Supervised Learning from Noisy and Incomplete Data
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tac...
arxiv.org
January 8, 2026 at 12:37 PM
📖 We put together with Mike Davies a review of self-supervised learning for inverse problems, covering the main approaches in the literature with a unified notation and analysis.
arxiv.org/abs/2601.03244
arxiv.org/abs/2601.03244
Can we train neural networks just with permutations of their initial weights? And then whats the best initialisation distribution ?
December 10, 2025 at 5:21 PM
Can we train neural networks just with permutations of their initial weights? And then whats the best initialisation distribution ?
Reposted by lebellig
PS: We also recently released a unified codebase for discrete diffusion, check it out!
𝕏 Thread : x.com/nkalyanv99/...
🔗 GitHub: github.com/nkalyanv99/...
📚 Docs: nkalyanv99.github.io/UNI-D2/
𝕏 Thread : x.com/nkalyanv99/...
🔗 GitHub: github.com/nkalyanv99/...
📚 Docs: nkalyanv99.github.io/UNI-D2/
December 9, 2025 at 4:06 PM
PS: We also recently released a unified codebase for discrete diffusion, check it out!
𝕏 Thread : x.com/nkalyanv99/...
🔗 GitHub: github.com/nkalyanv99/...
📚 Docs: nkalyanv99.github.io/UNI-D2/
𝕏 Thread : x.com/nkalyanv99/...
🔗 GitHub: github.com/nkalyanv99/...
📚 Docs: nkalyanv99.github.io/UNI-D2/
Reposted by lebellig
🆕 “Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction”
Huge thanks to Tobias Hoppe, @k-neklyudov.bsky.social,
@alextong.bsky.social, Stefan Bauer and @andreadittadi.bsky.social for their supervision! 🙌
arxiv : arxiv.org/abs/2512.05092 🧵👇
Huge thanks to Tobias Hoppe, @k-neklyudov.bsky.social,
@alextong.bsky.social, Stefan Bauer and @andreadittadi.bsky.social for their supervision! 🙌
arxiv : arxiv.org/abs/2512.05092 🧵👇
December 9, 2025 at 4:05 PM
🆕 “Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction”
Huge thanks to Tobias Hoppe, @k-neklyudov.bsky.social,
@alextong.bsky.social, Stefan Bauer and @andreadittadi.bsky.social for their supervision! 🙌
arxiv : arxiv.org/abs/2512.05092 🧵👇
Huge thanks to Tobias Hoppe, @k-neklyudov.bsky.social,
@alextong.bsky.social, Stefan Bauer and @andreadittadi.bsky.social for their supervision! 🙌
arxiv : arxiv.org/abs/2512.05092 🧵👇
I use drum mic kits for punchier presentations
December 8, 2025 at 8:53 AM
I use drum mic kits for punchier presentations
Then I’m counting on the sound engineer to engage
December 7, 2025 at 9:23 PM
Then I’m counting on the sound engineer to engage
"Improved Mean Flows: On the Challenges of Fastforward Generative Models" arxiv.org/abs/2512.02012 questions this approximation and proposes a new training process for mean flows.
I was intrigued by "Mean Flows for One-Step Generative Modeling" and, in particular, how it handles averaging the marginal velocity field during training. In practice, they don't and replace it with the conditional one in their loss function. I wonder how mismatches impact generation...
December 3, 2025 at 9:32 PM
"Improved Mean Flows: On the Challenges of Fastforward Generative Models" arxiv.org/abs/2512.02012 questions this approximation and proposes a new training process for mean flows.
Hi @ google, can you provide 100k TPU hours to explore the design space of diffusion bridges for image-to-image translation? x1 vs drift pred, architectures and # params, # dataset, scaling couplings and batch sizes (for minibatch-based couplings). I can run everything in jax in return...
December 3, 2025 at 9:19 PM
Hi @ google, can you provide 100k TPU hours to explore the design space of diffusion bridges for image-to-image translation? x1 vs drift pred, architectures and # params, # dataset, scaling couplings and batch sizes (for minibatch-based couplings). I can run everything in jax in return...
Reposted by lebellig
Yesterday, @nicolasdufour.bsky.social defended is PhD. I really enjoyed the years of collaboration w/ @vickykalogeiton.bsky.social (& @loicland.bsky.social)
Video: youtube.com/live/DXQ7FZA...
Big thanks to the jury @dlarlus.bsky.social @ptrkprz.bsky.social @gtolias.bsky.social A. Efros & T. Karras
Video: youtube.com/live/DXQ7FZA...
Big thanks to the jury @dlarlus.bsky.social @ptrkprz.bsky.social @gtolias.bsky.social A. Efros & T. Karras
November 27, 2025 at 7:14 PM
Yesterday, @nicolasdufour.bsky.social defended is PhD. I really enjoyed the years of collaboration w/ @vickykalogeiton.bsky.social (& @loicland.bsky.social)
Video: youtube.com/live/DXQ7FZA...
Big thanks to the jury @dlarlus.bsky.social @ptrkprz.bsky.social @gtolias.bsky.social A. Efros & T. Karras
Video: youtube.com/live/DXQ7FZA...
Big thanks to the jury @dlarlus.bsky.social @ptrkprz.bsky.social @gtolias.bsky.social A. Efros & T. Karras
Reposted by lebellig
@climateainordics.com is now on youtube! Check out some amazing talks on how to help fight climate change using AI!
youtube.com/@climateaino...
youtube.com/@climateaino...
♻️ Re-watch, re-learn, re-connect!
We are on the tube now! Check out the recordings from our first years events!
✨🍿🌍📺🌿🌊🌲☀️🌱🍄🌳
youtube.com/@climateaino...
We are on the tube now! Check out the recordings from our first years events!
✨🍿🌍📺🌿🌊🌲☀️🌱🍄🌳
youtube.com/@climateaino...
November 26, 2025 at 2:06 PM
@climateainordics.com is now on youtube! Check out some amazing talks on how to help fight climate change using AI!
youtube.com/@climateaino...
youtube.com/@climateaino...