DiffusionGemma Team, Adrien Ali Ta\"iga, James Assiene, Daniele Calandriello, Rahma Chaabouni, Jo\~ao Gante, Tamara von Glehn, Nate Keating, Chris Knutsen, Martin Kukla, Tianlin Liu, Ivan Lobov, Ofir Nabati, ... DiffusionGemma Technical Report https://arxiv.org/abs/2608.00146

Google DeepMind's DiffusionGemma Technical Report They feel text diffusion models open up a radically different part of the latency–quality Pareto frontier and hope the report makes it easier for researchers and engineers to understand the model, build on it, and create things we haven’t thought of

Co-organising a workshop: Non-Equilibrium Sampling: Diffusions · Flows · Particles September, Newcastle Come join the fun!

Newcastle Non-Equilibrium Sampling Workshop

Group photo from last year's Newcastle Sampling workshop.

sites.google.com

!!! sites.google.com/view/newcast...

🚨🧪 Announcing our #ICLR2026 Workshop, Generative AI in Genomics (Gen2): Barriers and Frontiers! @iclr-conf.bsky.social 📣Call for: Full workshop papers (5-8 pages) and Tiny papers (2-4 pages) 📅Submission deadline: 7 February 2026 AoE 🌐Learn more: genai-in-genomics.github.io (1/7)

Really interesting paper indeed.

Sam Power@spmontecarlo.bsky.social · 9mo ago

great stuff, right up my alley: arxiv.org/abs/2511.11497 'A Recursive Theory of Variational State Estimation: The Dynamic Programming Approach' - Filip Tronarp

🔥 WANTED: Student Researcher to join me, @vdebortoli.bsky.social, Jiaxin Shi, Kevin Li and @arthurgretton.bsky.social in DeepMind London. You'll be working on Multimodal Diffusions for science. Apply here google.com/about/career...

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.

Really nice.

Adrien Corenflos@adriencorenflos.bsky.social · 10mo ago

A little self promotion: Hai-Dang Dau (NUS) and I recently released this pre-print, which I'm not half proud of. arxiv.org/abs/2510.07559 The main problem we solve in it is to construct importance weights for Markov chain Monte Carlo. We achieve it via a method we call harmonization by coupling.

Very excited to share our preprint: Self-Speculative Masked Diffusions We speed up sampling of masked diffusion models by ~2x by using speculative sampling and a hybrid non-causal / causal transformer arxiv.org/abs/2510.03929 w/ @vdebortoli.bsky.social, Jiaxin Shi, @arnauddoucet.bsky.social

(1/n)🚨Train a model solving DFT for any geometry with almost no training data Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data! 📜 arxiv.org/abs/2506.01225 💻 github.com/majhas/self-...

Shunichi Amari has been awarded the 40th (2025) Kyoto Prize in recognition of his pioneering research in the fields of artificial neural networks, machine learning, and information geometry www.riken.jp/pr/news/2025...

甘利 俊一 栄誉研究員が「京都賞」を受賞

甘利 俊一栄誉研究員（本務：帝京大学 先端総合研究機構 特任教授）は、人工ニューラルネットワーク、機械学習、情報幾何学分野での先駆的な研究が評価され、第40回（2025）京都賞（先端技術部門　受賞対象分野：情報科学）を受賞しました。

riken.jp

🌟Applications open- LOGML 2025🌟 👥Mentor-led projects, expert talks, tutorials, socials, and a networking night ✍️Application form: logml.ai 🔬Projects: www.logml.ai/projects.html 📅Apply by 6th April 2025 ✉️Questions? logml.committee@gmail.com #MachineLearning #SummerSchool #LOGML #Geometry

LOGML 2025

London Geometry and Machine Learning Summer School, July 7-11 2025

logml.ai

SuperDiff goes super big! - Spotlight at #ICLR2025!🥳 - Stable Diffusion XL pipeline on HuggingFace huggingface.co/superdiff/su... made by Viktor Ohanesian - New results for molecules in the camera-ready arxiv.org/abs/2412.17762 Let's celebrate with a prompt guessing game in the thread👇

Why academia is sleepwalking into self-destruction. My editorial @brain1878.bsky.social If you agree with the sentiments please repost. It's important for all our sakes to stop the madness academic.oup.com/brain/articl...

Excited to see our paper “Computing Nonequilibrium Responses with Score-Shifted Stochastic Differential Equations” in Physical Review Letters this morning as an Editor’s Suggestion! We uses ideas from generative modeling to unravel a rather technical problem. 🧵 journals.aps.org/prl/abstract...

Great intro to PAC-Bayes bounds. Highly recommended!

Pierre Alquier@pierrealquier.bsky.social · last yr.

I already advertised for this document when I posted it on arXiv, and later when it was published. This week, with the agreement of the publisher, I uploaded the published version on arXiv. Less typos, more references and additional sections including PAC-Bayes Bernstein. arxiv.org/abs/2110.11216

Better diffusions with scoring rules! Fewer, larger denoising steps using distributional losses; learn the posterior distribution of clean samples given the noisy versions. arxiv.org/pdf/2502.02483 @vdebortoli.bsky.social Galashov Guntupalli Zhou @sirbayes.bsky.social @arnauddoucet.bsky.social

A standard ML approach for parameter estimation in latent variable models is to maximize the expectation of the logarithm of an importance sampling estimate of the intractable likelihood. We provide consistency/efficiency results for the resulting estimate: arxiv.org/abs/2501.08477

On the Asymptotics of Importance Weighted Variational Inference

For complex latent variable models, the likelihood function is not available in closed form. In this context, a popular method to perform parameter estimation is Importance Weighted Variational Infere...

arxiv.org

Speculative sampling accelerates inference in LLMs by drafting future tokens which are verified in parallel. With @vdebortoli.bsky.social , A. Galashov & @arthurgretton.bsky.social , we extend this approach to (continuous-space) diffusion models: arxiv.org/abs/2501.05370

🔊 Super excited to announce the first ever Frontiers of Probabilistic Inference: Learning meets Sampling workshop at #ICLR2025 @iclr-conf.bsky.social! 🔗 website: sites.google.com/view/fpiwork... 🔥 Call for papers: sites.google.com/view/fpiwork... more details in thread below👇 🧵

Schrödinger Bridge Flow for Unpaired Data Translation (by @vdebortoli.bsky.social et al.) It will take me some time to digest this article fully, but it's important to follow the authors' advice and read the appendices, as the examples are helpful and well-illustrated. 📄 arxiv.org/abs/2409.09347

The slides of my NeurIPS lecture "From Diffusion Models to Schrödinger Bridges - Generative Modeling meets Optimal Transport" can be found here drive.google.com/file/d/1eLa3...

One #postdoc position is still available at the National University of Singapore (NUS) to work on sampling, high-dimensional data-assimilation, and diffusion/flow models. Applications are open until the end of January. Details: alexxthiery.github.io/jobs/2024_di...

I have updated my course notes on Optimal Transport with a new Chapter 9 on Wasserstein flows. It includes 3 illustrative applications: training a 2-layer MLP, deep transformers, and flow-matching generative models. You can access it here: mathematical-tours.github.io/book-sources...

exciting new work by my truly brilliant postdoc Eugenio Clerico on the optimality of coin-betting strategies for mean estimation! for fans of: mean estimation, online learning with log loss, optimal portfolios, hypothesis testing with E-values, etc. dig in: arxiv.org/abs/2412.02640

On the optimality of coin-betting for mean estimation

Confidence sequences are sequences of confidence sets that adapt to incoming data while maintaining validity. Recent advances have introduced an algorithmic formulation for constructing some of the ti...

arxiv.org