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Michael Jordan gave a short, excellent, and provocative talk recently in Paris - here's a few key ideas - It's all just machine learning (ML) - the AI moniker is hype - The late Dave Rumelhart should've received a Nobel prize for his early ideas on making backprop work 1/n

🌶️spicy paper alert🌶️ We present TABASCO, a new model for small molecule generation that achieves state-of-the-art PoseBusters validity while also being ~10x faster This is all achieved despite ✖️no equivariance ✖️no self-conditioning ✖️no bond modelling what we found🧵(1/n)

1/10 ML can solve PDEs – but precision🔬is still a challenge. Towards high-precision methods for scientific problems, we introduce BWLer 🎳, a new architecture for physics-informed learning achieving (near-)machine-precision (up to 10⁻¹² RMSE) on benchmark PDEs. 🧵How it works:

I converted one of my favorite talks I've given over the past year into a blog post. "On the Tradeoffs of SSMs and Transformers" (or: tokens are bullshit) In a few days, we'll release what I believe is the next major advance for architectures.

Flow Matching (FM) is one of the hottest ideas in generative AI - and it’s everywhere at #ICML2025. But what is it? And why is it so elegant? 🤔 This thread is an animated, intuitive intro into (Variational) Flow Matching - no dense math required. Let's dive in! 🧵👇

Just in case someone was wondering why many Chinese products are becoming better and cheaper than European products, and it’s not only cars. Europe prefers to spend money on welfare and green projects instead. Good luck.

Bit of a late update, but I finally had my PhD viva! It was an honour and a privilege to have my thesis read and examined by Petar Veličković and Johannes Brandstetter, two giants in Geometric Deep Learning. It will soon be online after corrections :)

Very excited to announce our new tabular foundation model, Mitra!