📢 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
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/
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.
lebellig
Nicolas Audebert
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...
👏 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...
🎆 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/
Al Merose
"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
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/
Sander Dieleman
openathena.ai
Open Athena is a nonprofit that accelerates academia with capabilities from the AI frontier
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
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...
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...
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...
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 📉
Gilles Louppe
Congrats again to authors of accepted #ICML2026 papers! The camera-ready deadline is 5/28. Drawing your attention to two specific features:
1. As last year, to help communicate research to a broad audience, papers will have lay summaries. Tips & details in blog 1/3
