Best lab in the most wonderful city: the perfect opportunity! Come join us 🦾🌟

thank you ❤️

Checkout this cute blogpost on Jacobi fields! They were used to link Variational and Riemannian flow matching in Olga’s paper ☀️

My hope is that by making this concept more intuitive, we’ll see it unlock new applications in future ML research. Feedback welcome! :)

Riemannian Variational Flow Matching for Material and Protein Design We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the benefits of VFM, we d...

In case you are wondering: they aren't just a theoretical curiosity! We actually relied on Jacobi fields to prove key results in our recent paper, Riemannian Variational Flow Matching for Material and Protein Design 🪩 (arxiv.org/abs/2502.129...).

🔮 Working on ML on curved manifolds? Don't miss out on Jacobi Fields! 🔮

I wrote a quick, highly visual and hopefully accessible introduction to the topic: "Jacobi Fields in Machine Learning" 🤠 Check it out here: olgatticus.github.io/blog/jacobi-...!

My hope is that by making this concept more intuitive, we’ll see it unlock new applications in future ML research. Feedback welcome! :)

Riemannian Variational Flow Matching for Material and Protein Design We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the benefits of VFM, we d...

In case you are wondering: they aren't just a theoretical curiosity! We actually relied on Jacobi fields to prove key results in our recent paper, Riemannian Variational Flow Matching for Material and Protein Design 🪩 (arxiv.org/abs/2502.129...).

2 PhD Positions on Learning Causally Grounded Concepts for Safe AI Are you interested in improving the interpretability, robustness and safety of AI by integrating causal reasoning? The Causality team in the AMLab group at the University of Amsterdam is looking for 2...

🚨2 PhD positions with me @amlab.bsky.social on learning causally grounded concepts 🚨

Are you interested in improving the #interpretability #robustness and #safety of AI by integrating #causal reasoning? Join us in beautiful Amsterdam 🇳🇱🌷🚲

Deadline: 20 April

www.academictransfer.com/en/jobs/3593...

Interested in the analysis of GNN expressive power, limitations of the WL test or more in general in the definition of complete metrics on graphs? 👀

Check out this super cool work led by
@martin.topology.rocks !🔥

have fun!!! 🔥

Super happy to share our work was accepted as an Oral at the Delta Workshop @ ICLR 2025! 🎉

Can’t wait to talk about it in Singapore 😎

Congrats to the amazing team @eijkelboomfloor.bsky.social @alisometry.bsky.social @erikjbekkers.bsky.social 🔥

🤹 Excited to share Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems

joint work with @wellingmax.bsky.social and @jwvdm.bsky.social

preprint: arxiv.org/abs/2502.17019
code: github.com/maxxxzdn/erwin

Super excited about this project!! Amazing work by @olgatticus.bsky.social and others ❤️

Stay tuned for further analyses and experiments! ✨

A huge thanks to my brilliant collaborators for all their efforts and invaluable guidance: @eijkelboomfloor.bsky.social, @alisometry.bsky.social and @erikjbekkers.bsky.social 🔥

🧵5/5

We test our method on a checkerboard distribution on the sphere 🏁🌐, yielding promising results when compared to Euclidean and non-variational methods.

🧵4/5

By directly encoding the manifold, we introduce Riemannian-Gaussian-VFM, offering a fresh perspective on geometry-informed generative modeling. We also discuss how our approach relates to the previously mentioned methods. 🌍🌎🌏

🧵3/5

Inspired by Riemannian Flow Matching, we extend the Variational Flow Matching (VFM) objective to build a geometry-aware flow model. 🌊

Interestingly, VFM is particularly well-suited for explicitly parametrizing the support manifold of the distribution! 💅💪

🧵2/5

Variational Flow Matching goes Riemannian! 🔮

In this preliminary work, we derive a variational objective for probability flows 🌀 on manifolds with closed-form geodesics, and discuss some interesting results.

Dream team: Floor, Alison & Erik (their @ below) 💥

📜 arxiv.org/abs/2502.12981
🧵1/5

Welcome to our Bluesky account! 🦋

We're excited to announce ComBayNS workshop: Combining Bayesian & Neural Approaches for Structured Data 🌐

Submit your paper and join us in Rome for #IJCNN2025! 🇮🇹

📅 Papers Due: March 20th, 2025 📜
Webpage: combayns2025.github.io

🌟 Call for Papers – ComBayNS Workshop at IJCNN 2025 🌟

Workshop page: combayns2025.github.io

We are thrilled to announce the #workshop “ComBayNS: Combining Bayesian and Neural approaches for Structured data” at the upcoming International Joint Conference on Neural Networks (#IJCNN).

A thread 🧵

This is how it should be: one file, ctrl+c ctrl+v runnable from a notebook. I really do not want to mess with omegaconf, hydra or, the worst, a custom configuration system just to figure out how your model runs.

👮‍♀️🚨🚔

❤️❤️❤️

of course !!

we also celebrate it in some areas of Italy - like where I come from - where "Santa Lucia" is even more popular than Santa Claus for children!!

haters will say photoshop!!

If you are attending #NeurIPS2024🇨🇦, make sure to check out AMLab's 11 accepted papers ...and to have a chat with our members there! 👩‍🔬🍻☕

Submissions include generative modelling, AI4Science, geometric deep learning, reinforcement learning and early exiting. See the thread for the full list!

🧵1 / 12

So cooooool!!

Come and check out our poster on the UniReps workshop at @neuripsconf.bsky.social !

Thanks again to the amazing Giovanni Marchetti, Martina Scolamiero, and Danica Kragic ❤️

See more at: arxiv.org/abs/2409.10967