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/...
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 🧵👇
"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...
Reposted by lebellig
@neuripsconf.bsky.social is two weeks away!
📢 Stop missing great workshop speakers just because the workshop wasn’t on your radar. Browse them all in one place:
robinhesse.github.io/workshop_spe...
(also available for @euripsconf.bsky.social)
#NeurIPS #EurIPS
📢 Stop missing great workshop speakers just because the workshop wasn’t on your radar. Browse them all in one place:
robinhesse.github.io/workshop_spe...
(also available for @euripsconf.bsky.social)
#NeurIPS #EurIPS
November 19, 2025 at 8:00 PM
@neuripsconf.bsky.social is two weeks away!
📢 Stop missing great workshop speakers just because the workshop wasn’t on your radar. Browse them all in one place:
robinhesse.github.io/workshop_spe...
(also available for @euripsconf.bsky.social)
#NeurIPS #EurIPS
📢 Stop missing great workshop speakers just because the workshop wasn’t on your radar. Browse them all in one place:
robinhesse.github.io/workshop_spe...
(also available for @euripsconf.bsky.social)
#NeurIPS #EurIPS
Calling it for today... I tried using the gemini 3 Pro preview to build some js animations, and it went well
November 18, 2025 at 8:17 PM
Calling it for today... I tried using the gemini 3 Pro preview to build some js animations, and it went well
Interpolation between two gaussian distributions on a flat torus (my personal benchmark for new llms)
November 18, 2025 at 6:43 PM
Interpolation between two gaussian distributions on a flat torus (my personal benchmark for new llms)
It may not top ImageNet benchmarks, but honestly, that hardly matters... the removal of the VAE component is a huge relief and makes it much easier to apply diffusion models to domain-specific datasets that lack large-scale VAEs.
"Back to Basics: Let Denoising Generative Models Denoise" by
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
November 18, 2025 at 5:10 PM
It may not top ImageNet benchmarks, but honestly, that hardly matters... the removal of the VAE component is a huge relief and makes it much easier to apply diffusion models to domain-specific datasets that lack large-scale VAEs.
"Back to Basics: Let Denoising Generative Models Denoise" by
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
November 18, 2025 at 5:05 PM
"Back to Basics: Let Denoising Generative Models Denoise" by
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
Tianhong Li & Kaiming He arxiv.org/abs/2511.13720
Diffusion models in pixel-space, without VAE, with clean image prediction = nice generation results. Not a new framework but a nice exploration of the design space of the diffusion models.
Reposted by lebellig
We figured out flow matching over states that change dimension. With "Branching Flows", the model decides how big things must be! This works wherever flow matching works, with discrete, continuous, and manifold states. We think this will unlock some genuinely new capabilities.
November 10, 2025 at 9:10 AM
We figured out flow matching over states that change dimension. With "Branching Flows", the model decides how big things must be! This works wherever flow matching works, with discrete, continuous, and manifold states. We think this will unlock some genuinely new capabilities.
Reposted by lebellig
We created a 1-hour live-coding tutorial to get started in imaging problems with AI, using the deepinverse library
youtu.be/YRJRgmXV8_I?...
youtu.be/YRJRgmXV8_I?...
DeepInverse tutorial - computational imaging with AI
YouTube video by DeepInverse
youtu.be
November 13, 2025 at 3:24 PM
We created a 1-hour live-coding tutorial to get started in imaging problems with AI, using the deepinverse library
youtu.be/YRJRgmXV8_I?...
youtu.be/YRJRgmXV8_I?...
I’ll be at EurIPS in Copenhagen in early December ! Always up for chats about diffusion, flow matching, Earth observation, AI4climate, etc... Ping me if you’re going! 🇩🇰🌍
November 12, 2025 at 9:07 PM
I’ll be at EurIPS in Copenhagen in early December ! Always up for chats about diffusion, flow matching, Earth observation, AI4climate, etc... Ping me if you’re going! 🇩🇰🌍
I first came across the idea of learning curved interpolants in "Branched Schrödinger Bridge Matching" arxiv.org/abs/2506.09007. I liked it, but I’m curious how well it scales to high-dim settings and how challenging it is to learn sufficiently good interpolants to train the diffusion bridge
November 12, 2025 at 8:57 PM
I first came across the idea of learning curved interpolants in "Branched Schrödinger Bridge Matching" arxiv.org/abs/2506.09007. I liked it, but I’m curious how well it scales to high-dim settings and how challenging it is to learn sufficiently good interpolants to train the diffusion bridge
"Curly Flow Matching for Learning Non-gradient Field Dynamics" @kpetrovvic.bsky.social et al. arxiv.org/pdf/2510.26645
Solving the Schrödinger bridge pb with a non-zero drift ref. process: learn curved interpolants, apply minibatch OT with the induced metric, learn the mixture of diffusion bridges.
Solving the Schrödinger bridge pb with a non-zero drift ref. process: learn curved interpolants, apply minibatch OT with the induced metric, learn the mixture of diffusion bridges.
November 12, 2025 at 8:09 PM
"Curly Flow Matching for Learning Non-gradient Field Dynamics" @kpetrovvic.bsky.social et al. arxiv.org/pdf/2510.26645
Solving the Schrödinger bridge pb with a non-zero drift ref. process: learn curved interpolants, apply minibatch OT with the induced metric, learn the mixture of diffusion bridges.
Solving the Schrödinger bridge pb with a non-zero drift ref. process: learn curved interpolants, apply minibatch OT with the induced metric, learn the mixture of diffusion bridges.
Reposted by lebellig
I'm on my way to @caltech.edu for an AI + Science conference. Looking forward to seeing some friends and meeting new ones. There will be a livestream.
aiscienceconference.caltech.edu
aiscienceconference.caltech.edu
November 9, 2025 at 8:41 PM
I'm on my way to @caltech.edu for an AI + Science conference. Looking forward to seeing some friends and meeting new ones. There will be a livestream.
aiscienceconference.caltech.edu
aiscienceconference.caltech.edu
Reposted by lebellig
“Entropic (Gromov) Wasserstein Flow Matching with GENOT” by D. Klein et al. arxiv.org/abs/2310.09254
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
October 30, 2025 at 10:43 PM
“Entropic (Gromov) Wasserstein Flow Matching with GENOT” by D. Klein et al. arxiv.org/abs/2310.09254
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Reposted by lebellig
💥 DeepInverse is now part of the official PyTorch Landscape💥
We are excited to join an ecosystem of great open-source AI libraries, including @hf.co diffusers, MONAI, einops, etc.
pytorch.org/blog/deepinv...
We are excited to join an ecosystem of great open-source AI libraries, including @hf.co diffusers, MONAI, einops, etc.
pytorch.org/blog/deepinv...
DeepInverse Joins the PyTorch Ecosystem: the library for solving imaging inverse problems with deep learning – PyTorch
pytorch.org
November 5, 2025 at 5:31 PM
💥 DeepInverse is now part of the official PyTorch Landscape💥
We are excited to join an ecosystem of great open-source AI libraries, including @hf.co diffusers, MONAI, einops, etc.
pytorch.org/blog/deepinv...
We are excited to join an ecosystem of great open-source AI libraries, including @hf.co diffusers, MONAI, einops, etc.
pytorch.org/blog/deepinv...
Reposted by lebellig
🌀🌀🌀New paper on the generation phases of Flow Matching arxiv.org/abs/2510.24830
Are FM & diffusion models nothing else than denoisers at every noise level?
In theory yes, *if trained optimally*. But in practice, do all noise level equally matter?
with @annegnx.bsky.social, S Martin & R Gribonval
Are FM & diffusion models nothing else than denoisers at every noise level?
In theory yes, *if trained optimally*. But in practice, do all noise level equally matter?
with @annegnx.bsky.social, S Martin & R Gribonval
November 5, 2025 at 9:03 AM
🌀🌀🌀New paper on the generation phases of Flow Matching arxiv.org/abs/2510.24830
Are FM & diffusion models nothing else than denoisers at every noise level?
In theory yes, *if trained optimally*. But in practice, do all noise level equally matter?
with @annegnx.bsky.social, S Martin & R Gribonval
Are FM & diffusion models nothing else than denoisers at every noise level?
In theory yes, *if trained optimally*. But in practice, do all noise level equally matter?
with @annegnx.bsky.social, S Martin & R Gribonval
Reposted by lebellig
Want to work on generative models and Earth Observation? 🌍
I'm looking for:
🧑💻 an intern on generative models for change detection
🧑🔬 a PhD student on neurosymbolic generative models for geospatial data
Both starting beginning of 2026.
Details are below, feel free to email me!
I'm looking for:
🧑💻 an intern on generative models for change detection
🧑🔬 a PhD student on neurosymbolic generative models for geospatial data
Both starting beginning of 2026.
Details are below, feel free to email me!
November 4, 2025 at 10:08 AM
Want to work on generative models and Earth Observation? 🌍
I'm looking for:
🧑💻 an intern on generative models for change detection
🧑🔬 a PhD student on neurosymbolic generative models for geospatial data
Both starting beginning of 2026.
Details are below, feel free to email me!
I'm looking for:
🧑💻 an intern on generative models for change detection
🧑🔬 a PhD student on neurosymbolic generative models for geospatial data
Both starting beginning of 2026.
Details are below, feel free to email me!
Reposted by lebellig
We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control.
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
October 31, 2025 at 11:24 AM
We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control.
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
“Entropic (Gromov) Wasserstein Flow Matching with GENOT” by D. Klein et al. arxiv.org/abs/2310.09254
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
October 30, 2025 at 10:43 PM
“Entropic (Gromov) Wasserstein Flow Matching with GENOT” by D. Klein et al. arxiv.org/abs/2310.09254
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Transport between two distributions defined on different spaces by training a noise-to-data flow models in the target space, conditioned on the source data and leveraging Gromov–Wasserstein couplings
Reposted by lebellig
New paper, with @rkhashmani.me @marielpettee.bsky.social @garrettmerz.bsky.social Hellen Qu. We introduce a framework for generating realistic, highly multimodal datasets with explicitly calculable mutual information. This is helpful for studying self-supervised learning
arxiv.org/abs/2510.21686
arxiv.org/abs/2510.21686
October 28, 2025 at 5:23 PM
New paper, with @rkhashmani.me @marielpettee.bsky.social @garrettmerz.bsky.social Hellen Qu. We introduce a framework for generating realistic, highly multimodal datasets with explicitly calculable mutual information. This is helpful for studying self-supervised learning
arxiv.org/abs/2510.21686
arxiv.org/abs/2510.21686