Johannes Brandstetter

According to our database, Johannes Brandstetter authored at least 34 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.

Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter, Sepp Hochreiter, Andreas Mayr, Günter Klambauer

CoRR, 2024

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework.

Artur P. Toshev, Harish Ramachandran, Jonas A. Erbesdobler, Gianluca Galletti, Johannes Brandstetter, Nikolaus A. Adams

CoRR, 2024

GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks.

Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr

CoRR, 2024

Clifford-Steerable Convolutional Neural Networks.

Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré

CoRR, 2024

Geometry-Informed Neural Networks.

Arturs Berzins, Andreas Radler, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter

CoRR, 2024

Universal Physics Transformers.

Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter

CoRR, 2024

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations.

Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter

CoRR, 2024

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics.

Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter

CoRR, 2024

2023

E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics.

Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams

CoRR, 2023

G-Signatures: Global Graph Propagation With Randomized Signatures.

Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter, Sepp Hochreiter

CoRR, 2023

Clifford Group Equivariant Neural Networks.

David Ruhe, Johannes Brandstetter, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers.

Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Lie Point Symmetry and Physics-Informed Networks.

Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Geometric Clifford Algebra Networks.

David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter

Proceedings of the International Conference on Machine Learning, 2023

ClimaX: A foundation model for weather and climate.

Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover

Proceedings of the International Conference on Machine Learning, 2023

Clifford Neural Layers for PDE Modeling.

Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Boundary Graph Neural Networks for 3D Simulations.

Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

Jayesh K. Gupta, Johannes Brandstetter

CoRR, 2022

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution.

Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, José Antonio Arjona-Medina, Sepp Hochreiter

Proceedings of the International Conference on Machine Learning, 2022

Lie Point Symmetry Data Augmentation for Neural PDE Solvers.

Johannes Brandstetter, Max Welling, Daniel E. Worrall

Proceedings of the International Conference on Machine Learning, 2022

Message Passing Neural PDE Solvers.

Johannes Brandstetter, Daniel E. Worrall, Max Welling

Proceedings of the Tenth International Conference on Learning Representations, 2022

Geometric and Physical Quantities improve E(3) Equivariant Message Passing.

Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling

Proceedings of the Tenth International Conference on Learning Representations, 2022

Few-Shot Learning by Dimensionality Reduction in Gradient Space.

Martin Gauch, Maximilian Beck, Thomas Adler, Dmytro Kotsur, Stefan Fiel, Hamid Eghbal-zadeh, Johannes Brandstetter, Johannes Kofler, Markus Holzleitner, Werner Zellinger, Daniel Klotz, Sepp Hochreiter, Sebastian Lehner

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER.

Markus Holzleitner, Lukas Gruber, José Antonio Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter

Trans. Large Scale Data Knowl. Centered Syst., 2021

Learning 3D Granular Flow Simulations.

Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

CoRR, 2021

Hopfield Networks is All You Need.

Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David P. Kreil, Michael K. Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks.

Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams

Proceedings of the Geometric Science of Information - 6th International Conference, 2021

2020

Cross-Domain Few-Shot Learning by Representation Fusion.

Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David P. Kreil, Michael Kopp, Günter Klambauer, Sepp Hochreiter

CoRR, 2020

Hopfield Networks is All You Need.

Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Milena Pavlovic, Geir Kjetil Sandve, Victor Greiff, David P. Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter

CoRR, 2020

Modern Hopfield Networks and Attention for Immune Repertoire Classification.

Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

XAI and Strategy Extraction via Reward Redistribution.

Marius-Constantin Dinu, Markus Hofmarcher, Vihang Prakash Patil, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

Using LSTMs for climate change assessment studies on droughts and floods.

Frederik Kratzert, Daniel Klotz, Johannes Brandstetter, Pieter-Jan Hoedt, Grey Nearing, Sepp Hochreiter

CoRR, 2019

Quantum Optical Experiments Modeled by Long Short-Term Memory.

Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter

CoRR, 2019

RUDDER: Return Decomposition for Delayed Rewards.

Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019