Anshuk Uppal
@sigmabayesian
PhD student @DTUtweet. Probabilistic ML 🧠 diffusion and sampling🧠. previously intern @MSFTResearch @SonyAI_global, visitor @NYU_Courant.
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GeMSS 2026 will come to London from Mar 23 - Mar 27, apply by Jan 23 - imho no.1 research school in Europe on generative models - 3-day crash course on foundations - 2-day frontier talks (diffusion, LLM for math, AI4Science, etc.) @jesfrellsen @pamattei @jmtomczak @bguedj
🎁 Christmas gift: let’s learn deep generative models through 101 papers! Advanced Topics of Deep Generative Models: a hybrid of lectures + student talks, spanning foundations to applications. Today, the slides are public: jiataogu.me/DeepGenerative… Huge thanks to @xxunhuang…
jiataogu.me
Advanced Topics of Deep Generative Models
Graduate-level course on advanced topics in deep generative models. Instructor: Jiatao Gu. Towne Building 337, Monday & Wednesday 3:30-5:00 PM.
Performance Hints Over the years, my colleague Sanjay Ghemawat and I have done a fair bit of diving into performance tuning of various pieces of code. We wrote an internal Performance Hints document a couple of years ago as a way of identifying some general principles and we've…
Heading to neurips soon! I'm on the job market and I work on making AI reliable, with a focus on healthcare. Would love to meet people and hear about opportunities!
OK, I can organise re diffusion circle at EurIPS, and tweet, if you can RT 🙏 then this would reach to more people.
🔥 WANTED: Student Researcher to join me,@ValentinDeBort1,@thjashin,@liwenliang,@ArthurGretton in DeepMind London. You'll be working on Multimodal Diffusions for science. Apply here google.com/about/careers/…
Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on! 📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon. It traces the core…
✨Masked Diffusion Language Models✨ are great for reasoning, but not just for the reasons you think! Fast parallel decoding? 🤔 Any-order decoding? 🤨 Plot twist: MDLMs offer A LOT MORE for inference and post-training! 🎢🧵
Looking forward to this workshop on ML4molecules at the ELLIS unconference (followed by EurIPS). Please submit your abstracts! The deadline will be extended to 15 October 2025.
ELLIS ML4Molecules workshop 2025 ** Call for papers** is out: moleculediscovery.github.io/workshop2025/
Consistency models, CTMs, shortcut models, align your flow, mean flow... What's the connection, and how should you learn them in practice? We show they're all different sides of the same coin connected by one central object: the flow map. arxiv.org/abs/2505.18825 🧵(1/n)
GDM is growing in APAC, this time in Singapore🇸🇬 Come join us to build AI responsibly to benefit users in APAC & beyond! Research Lead & Director: job-boards.greenhouse.io/deepmind/jobs/… Research Scientist & Engineer (Multimodal GenAI): job-boards.greenhouse.io/deepmind/jobs/… job-boards.greenhouse.io/deepmind/jobs/…
Oh yea! Forgot to mention - it's also a one-step diffusion model 😉
🧵generative models are sweet, but navigating existing repositories can be overwhelming, particularly when starting a new research project so i built jax-interpolants, a clean & flexible implementation of the stochastic interpolant framework in jax github.com/nmboffi/jax-in…
🔬 Interested in AI4Science? 📢 2 Funded PhD positions at TU Wien in Learning on Graphs & Geometry with AITHYRA! 🗓 Apply by Sept 4, 2025 📍 Vienna, Austria 🔗 jobs.tuwien.ac.at/Job/256399 #PhD #AI4Science #MachineLearning #GeometricDeepLearning #TUWien #AITHYRA
Lucky to be part of this incredible piece with summary of progress on many hot AI for Science areas!
Our 500+ page AI4Science paper is finally published: Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Foundations and Trends® in Machine Learning, Vol. 18, No. 4, 385–912, 2025 nowpublishers.com/article/Detail…
In an hour, François and I are presenting at ICML our paper on crystalline material generation using diffusion models, where the key innovation is a diffusion process for the fractional coordinates that is inspired by kinetic Langevin dynamics. Paper: arxiv.org/abs/2507.03602
This new work generalizes the recent Adjoint Sampling approach from Stochastic Control to Schrodinger Bridges, enabling measure transport between data and unnormalized densities. Achieves SOTA on large-scale energy-driven conformer generation. See thread by @guanhorng_liu
Adjoint-based diffusion samplers have simple & scalable objectives w/o impt weight complication. Like many, though, they solve degenerate Schrödinger bridges, despite all being SB-inspired. 📢 Proudly introduce #Adjoint #Schrödinger #Bridge #Sampler, a full SB-based sampler that…
I'm fortunate to be able to devote my career to researching AI and building reasoning models like o3 for the world to use. If you want to join us in pushing forward the intelligence frontier, we're hiring at @OpenAI.
What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold? In a new preprint with @ZKadkhodaie @EeroSimoncelli, we develop a novel energy-based model in order to answer these questions: 🧵
We REALLY REALLY need a "Findings" for NeurIPS, ICLR, and ICML. 25,000 submissions at this year's NeurIPS represents extreme excess pressure. It takes valuable time away from legitimate new research. One question is how to administer it. I suggest that Findings go through a…
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