mholz314

Postdoc interested in Mathematical Physics and Machine Learning Theory

[Bahram Shakerin @BahramShakerin]

Terence Tao

https://t.co/J19zRioVP7

[Johannes Brandstetter @jo_brandstetter]

General relativity 🤝 neural fields

This simulation of a black hole is coming from our neural networks 🚀

We introduce Einstein Fields, a compact NN representation for 4D numerical relativity. EinFields are designed to handle the tensorial properties of GR and its derivatives.

[Johannes Brandstetter @jo_brandstetter]

Today marks a big milestone for us at Emmi AI. We’ve raised a €15M seed round, backed by 3VC, Speedinvest, Serena, and PUSH. Let’s build the future of Physics AI together!

[Johannes Brandstetter @jo_brandstetter]

Super hyped to share NeuralDEM -- the first real-time simulation of industrial particulate flows. NeuralDEM replaces Discrete Element Method (DEM) routines and coupled (CFD-DEM) multiphysics simulations. 🧵

📜: https://t.co/JH4PDpth5g
🖥️: https://t.co/VEsawzd9IV

[Johannes Brandstetter @jo_brandstetter]

Interesting in scaling up neural operators? Happy to announce that Universal Physics Transformers (UPT) -- a scalable framework for neural operators is accepted at #neurips2024.

Paper: https://t.co/fMAVb42UjD
Project page: https://t.co/8Vj2scLeeC

[Johannes Brandstetter @jo_brandstetter]

We introduce Geometry-Informed Neural Networks to train shape generative models
without any data (!!), combining learning under constraints, neural fields as a suitable representation, and generating diverse solutions to under-determined problems:
🖥️: https://t.co/qRbJ9SXuc0

[David Ruhe @djjruhe]

New work on Geometric Clifford Algebra Networks (GCANs). We propose geometric templates for modeling dynamical systems. A 🧵on geometric / Clifford algebras, and symmetry group transformations in neural networks.
📜https://t.co/ugW5HAgFi6

[Marius-Constantin Dinu @DinuMariusC]

Our paper "Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation" has been selected for oral presentation (notable-top-5%) at #ICLR2023 https://t.co/M0SnPF6Ufq. [1/n]

[Johannes Brandstetter @jo_brandstetter]

New work on how to construct neural network layers on composite objects of scalars, vectors, bivectors, … --> multivectors! Via Clifford algebras, we generalize convolution and Fourier transforms to multivectors, especially relevant for PDE modeling:

https://t.co/51F2T3gnX0

[Martin Gauch @martingauch]

Interested in few-shot learning beyond miniImageNet? Deep nets often struggle to predict systems with varying parameters when data are scarce. Our new few-shot learning method SubGD is here to help!

https://t.co/kamNVNkwtH 🧵 1/5

[Johannes Brandstetter @jo_brandstetter]

Super interesting paper, a must read for everybody who is interested in domain adaptation. Very well done!

[Johannes Brandstetter @jo_brandstetter]

Are you interested in graph structured data?
Do you want to include geometry and physics to boost your GNNs?
Check out our paper on Steerable E(3) Equivariant Graph Neural Networks.
Joint work with @robdhess @ElisevanderPol @erikjbekkers @wellingmax

https://t.co/exBcDhQ44k

[Johannes Brandstetter @jo_brandstetter]

Great work lead by @AndreasMayr11. Granular flows learn to move inside complex geometric objects without any handcrafted restrictions. Paper: https://t.co/qKIBDKZVJZ Blog post: https://t.co/eWL1A7Awuv

[Dan Roberts @danintheory]

This formula accurately gives the output of a deep MLP after training.

11/

[Max Welling @wellingmax]

Fantastic work by the AMLAB team Johannes Brandstetter @jbrandi6, Rob Hesselink @robdhess, Elise van der Pol @ElisevanderPol and Erik Bekkers @erikjbekkers. We trained a fully steerable equivariant GNN to reach the top of the leaderboard. Congrats team.

[Andreu Vall @AndreuVall]

Great news! Our article reviewing machine learning approaches to drug-induced liver injury prediction has been published at @FrontAIBigData. https://t.co/t2zrpthRQm #ML #DrugDiscovery #DrugSafety #Hepatotoxicity #OpenAccess

[LittleBlackSheep @PhilipMWinter]

Our white paper "Trusted AI: Towards Certification of Machine Learning Applications" is now available on arxiv: https://t.co/C73Jqz5S3m

[Johannes Brandstetter @jo_brandstetter]

Our paper "Hopfield Networks is All You Need" is accepted at #ICLR2021. Time to give some talks :) I am very honored to present our research today at the great platform of @ml_collective @savvyRL (https://t.co/vb2n5cMjcL).

[Johannes Brandstetter @jo_brandstetter]

Want to hear what @DimaKrotov and Sepp Hochreiter say about Modern Hopfield Networks? Then join tomorrow's discussion @NeurIPSConf hosted by @IARAInews

[Michael Widrich @MichaelWidrich]

Tackling an extremely massive multiple instance learning problem in immunology with deep learning and attention (modern Hopfield networks!)
Check out our spotlight and poster at #neurips2020! 🙂
Dec 09, 07:10-07:20 (Spotlight), 09:00-11:00 (Poster) AM PST
https://t.co/PwQrdCgqkG