Erik Bekkers

Enschede crowd you don't want to miss this! Join us for two exciting talks by Jolanda Wentzel and @erikjbekkers.bsky.social. As a bonus, you get to see me in "appropriate traditional dress from [my] country of origin".

📅 28 May
📍 UTwente campus

Vacancy — PhD Position on Learning Concepts with Theoretical Guarantees Using Causality and RL Are you interested in improving the interpretability, robustness and safety of current AI systems? If the answer is yes, please continue reading!

New PhD position at the University of Amsterdam in @amlab.bsky.social on learning concepts with theoretical guarantees using #causality and #RL with me, Frans Oliehoek (TU Delft) and Herke van Hoof 💥

Deadline: 15 June

werkenbij.uva.nl/en/vacancies...

Symmetry, scale, and science: A geometric path to better AI The success of modern AI systems has been largely driven by massive scaling of data and compute resources. However, in scientific applications, where

💬 Join TODAY’s webinar: “Symmetry, scale, and science: A geometric path to better AI" by @erikjbekkers.bsky.social & @jobrandstetter.bsky.social
(ELLIS Program “Geometric Deep Learning”)

🗓️ Mon, March 10, 2025
🕓 16:00-17:00 CET

🔗 Register here: aiforgood.itu.int/event/symmet...

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 🔥

Synthetic data to enhance new treatment uptake for acute ischemic stroke - Responsible Digital Transformations Multiple (pre-)clinical trials are required in the regulatory trajectory to introduce new medical devices in clinical practice. The process is widely acknowledged as sub-optimal, resource-heavy, and t...

🚀 Excited to be part of this project led by Praneeta Konduri! We’re using state-of-the-art generative models to create synthetic vasculature geometries—pushing stroke treatment development forward while cutting down on patient data reliance. Exciting stuff! 😃 rdt.uva.nl/research/res...

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

Common beliefs about equivariant networks for image input include 1) They are slow. 2) They don’t scale to ImageNet. 3) They are complicated. In my opinion, these three are all false. To argue against them, we made minimal modifications to popular vision models, turning them mirror-equivariant.

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend ...

Really excited about this! We note a connection between diffusion/flow models and neural/latent SDEs. We show how to use this for simulation-free learning of fully flexible SDEs. We refer to this as SDE Matching and show speed improvements of several orders of magnitude.

arxiv.org/abs/2502.02472

✨ The VIS Lab at the #University of #Amsterdam is proud and excited to announce it has #TWELVE papers 🚀 accepted for the leading #AI-#makers conference on representation learning ( #ICLR2025 ) in Singapore 🇸🇬. 1/n
👇👇👇 @ellisamsterdam.bsky.social

Beyond Sequence: Impact of Geometric Context for RNA Property Prediction Accurate prediction of RNA properties, such as stability and interactions, is crucial for advancing our understanding of biological processes and developing RNA-based therapeutics. RNA structures can ...

Accepted to ICLR 🚨 Does using more geometry always help with molecule property prediction? In practice, we deal with imperfect geometries, which introduce structural noise.

In our work arxiv.org/abs/2410.11933, we investigate when and how geometric information is useful (or not) for RNA molecules.

Grounding Continuous Representations in Geometry: Equivariant... Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone...

🔥 3/4 #ICLR2025 Grounding Continuous Representations in Geometry: Equivariant Neural Fields (ENF), for geometry-informed continuous signal representations openreview.net/forum?id=A4e...
+@dafidofff.bsky.social, @davidmknigge.bsky.social @erikjbekkers.bsky.social S. Papa, R. Valperga, S. Vadgama

Unlearning Toxicity in Multimodal Foundation Models & Learning to design protein-protein interactions with enhanced generalization Part 1 (Rita Cucchiara): Foundation Models, pretrained on extremely large unknown source of data, contain in their embed space many information

🚀 AI for Good Webinar Launch: From Molecules to Models

Next week, ELLIS kicks off a new webinar series on AI in life sciences, showcasing key research from ELLIS Programs.

🗓️ Date: Feb 3, 2025
🕓 Time: 16:00-17:30 CET

Register: aiforgood.itu.int/event/unlear...

Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of in...

♻️Learning Symmetries via Weight-sharing with Doubly Stochastic Tensors

by Putri van der Linden, @algarciacast.bsky.social, @sharvaree.bsky.social, Thijs P. Kuipers, @erikjbekkers.bsky.social

🪪https://neurips.cc/virtual/2024/poster/96699
📜https://arxiv.org/abs/2412.04594

🧵9/ 12

🐑 Come and check out Variational Flow Matching for Graph Generation next week at @neuripsconf.bsky.social ! 🐑

Wed 11 Dec 11 a.m. PST — 2 p.m. PST
West Ballroom A-D #7103

arxiv.org/abs/2406.04843

Meet our Lab's members: staff, postdocs and PhD students! :)

With this starter pack you can easily connect with us and keep up to date with all the member's research and news 🦋

go.bsky.app/8EGigUy

A starter pack for researchers interested in Geometric Deep Learning - in the broadest sense possible!

Let me know if you would like to be listed. :)

Thanks @sharvaree.bsky.social for the idea!

go.bsky.app/7h8sek

Soon, @erikjbekkers.bsky.social and @davidmknigge.bsky.social will give a talk elaborating even further on geometry-grounded representation learning in a NeurReps seminar. Make sure to mark the date! :)

⏰ November 21st, 4 PM CET
🔗 www.neurreps.org/speaker-seri...