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Particularly the proof in chapter 4 (entirely Ed Wagstaff 's work) blows my mind🤯. He worked on this proof for an entire year and, imho, totally worth it. I love his talent for making abstract theory accessible for the reader. Blog post will follow 🙂 arxiv.org/abs/2107.01959 ☕️

Thank you for this great write-up about AF2! journals.iucr.org/d/issues/2021/… "The SE(3)-Transformer [...] is currently too computationally expensive for protein-scale tasks" ➡️ To be honest, I initially thought so as well; but Minkyung Baek et al made it work! 😍 science.sciencemag.org/content/sci/ea…

RoseTTAFold about to become 21X faster? "NVIDIA just released an open-source optimized implementation that uses 9x less memory and is up to 21x faster than the baseline official implementation." developer.nvidia.com/blog/accelerat…

Just to clarify. RoseTTAFold uses SE3 transformers (the core part of the structure module) and Nvidia just released a much faster version of this core part. The current code does not yet integrate these updates.

What startups are out there these days that need or make good use of the probabilistic ML toolkit? (thinking mostly: generative modeling, prob. inference, BO/active learning) Slowly starting to explore the job market and need your help! 🙏 RT

Metacognition in AI? We'd love for you to join us at #NeurIPS2021 and tell us about your work. Closing soon... #ai #artificialintelligence #ml #machinelearning #robotics #cogsci sites.google.com/view/metacogne…

Amazing video! Fantastic book, too, I am glad it receives all this attention. Many of those ideas & concepts are very fundamental and so helpful to understand, regardless of which specific sub-field of machine learning one is in.

Molecule Generation in 3D with Equivariant Diffusion (arxiv.org/abs/2203.17003). Very happy to share this project (the last of my PhD woohoo 🥳) and a super nice collab with Víctor Garcia Satorras @ClementVignac (equal contrib shared among three of us) and of course Max Welling

Graph neural networks often have to globally aggregate over all nodes. How we do this can have a significant impact on performance 🎯. After we recently finished a project on this, I wrote a blog post on this topic. Let me know what you think! :) ➡️fabianfuchsml.github.io/equilibriumagg… ☕️

New blog post! Find out: - what reconstructing masked images and our brains have in common, - why reconstructing masked images is a good idea for learning representations, - what makes a good mask and how to learn one akosiorek.github.io/ml/2022/07/04/…

Anyone know of a department looking to hire faculty in the protein/genome+evolution+ML space? Also RNA biology (asking for a friend) 🙂🥼🧪

I have recently had a range of very insightful conversations with Petar Veličković about graph neural networks, networks on sets, universality and how ideas have spread in the two communities. This is our write up, feedback welcome as always! :) ➡️fabianfuchsml.github.io/universalgraphs ☕️

📢 New blog post! Realising an intricate connection between PNA (Gabriele Corso Luca Cavalleri Dominique Beaini Pietro Lio') & the seminal work on set representations (Wagstaff Fabian Fuchs Martin Engelcke Ingmar Posner M A Osborne), Fabian and I join forces to attempt to explain!

Text-to-image diffusion models seem to have a good idea of geometry. Can we extract that geometry? Or maybe we can nudge these models to create large 3D consistent environments? Here's a blog summarizing some ideas in this space :) akosiorek.github.io/geometry_in_im…

Graphs , Sets, Universality We put more work into this and are presenting it via the ICLR blogpost track (thanks to organisers and reviewers!). Have a read and let us know what you think: iclr-blogposts.github.io/2023/blog/2023… better in light mode💡, dark mode🌙 messes with the latex a bit

Our Apple ML Research team in Barcelona is looking for a PhD intern! 🎓 Curiosity-driven research 🧠 with the goal to publish 📝 Topics: Confidence/uncertainty quantification and reliability of LLMs 🤖 Apple here: jobs.apple.com/en-gb/details/…