iiinova — where your digital self keeps evolving

Reposted by Kirill Neklyudov

Vincent Pauline @vincent-pauline.bsky.social · 09/12/2025

🆕 “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 🧵👇

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Reposted by Kirill Neklyudov

majhas.bsky.social @majhas.bsky.social · 10/06/2025

(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-...

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Kirill Neklyudov @k-neklyudov.bsky.social · 02/05/2025

David is disrupting everone's neurips grind by putting out amazing works, such a dirty trick!

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Reposted by Kirill Neklyudov

Martin Bauer @martinmbauer.bsky.social · 21/04/2025

Renormalisation is a central concept in modern physics. It describes how the dynamics of a system change at different scales. A great way to understand and visualise renormalisation is the Ising model (some math, but one can follow without it ) 1/13

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Kirill Neklyudov @k-neklyudov.bsky.social · 06/03/2025

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👇

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Kirill Neklyudov @k-neklyudov.bsky.social · 22/01/2025

We've been sharing these projects during the year, but today, they have been accepted at #ICLR2025 (1-3) and #AISTATS2025 (4)

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Kirill Neklyudov @k-neklyudov.bsky.social · 28/12/2024

🧵(1/7) Have you ever wanted to combine different pre-trained diffusion models but don't have time or data to retrain a new, bigger model? 🚀 Introducing SuperDiff 🦹‍♀️ – a principled method for efficiently combining multiple pre-trained diffusion models solely during inference!

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Reposted by Kirill Neklyudov

Alán Aspuru-Guzik @aspuru.bsky.social · 23/12/2024

10 minutes ago I am excited to share a perspective on the much-needed topic of hashtag#safety for hashtag#selfdrivinglaboratories. As the field progresses, understanding the challenges and gaps in building safe setups will be crucial for scaling up this technology! doi.org/10.26434/che...

doi.org

Steering towards safe self-driving laboratories

The past decade has witnessed remarkable advancements in autonomous systems, such as automobiles that are evolving from traditional vehicles to ones capable of navigating complex environments without ...

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Reposted by Kirill Neklyudov

Alexander Kolesnikov @handle.invalid · 20/12/2024

With some delay, JetFormer's *prequel* paper is finally out on arXiv: a radically simple ViT-based normalizing flow (NF) model that achieves SOTA results in its class. Jet is one of the key components of JetFormer, deserving a standalone report. Let's unpack: 🧵⬇️

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Kirill Neklyudov @k-neklyudov.bsky.social · 18/12/2024

Come join us in Singapore at #ICLR2025 to discuss the latest developments everywhere where Learning meets Sampling!

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Kirill Neklyudov @k-neklyudov.bsky.social · 10/12/2024

We're presenting our spotlight paper on transition path sampling at #NeurIPS2024 this week! Learn how to speed up the conventional Monte Carlo approaches by orders of magnitude Wed 11 Dec 4:30 pm #2606 arxiv.org/abs/2410.07974 first authors = {Yuanqi Du, Michael Plainer, @brekelmaniac.bsky.social}

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Kirill Neklyudov @k-neklyudov.bsky.social · 27/11/2024

so happy to see that Action Matching finds its applications in physics, outperforming diffusion models and Flow Matching! wonderful work by Jules Berman, Tobias Blickhan, and Benjamin Peherstorfer! arxiv.org/abs/2410.12000

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