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ORCIDJohannes Brandstetter
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2020 – today
- 2024
[c20]Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr:
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. Tiny Papers @ ICLR 2024- [c19]Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter:
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics. ICML 2024 - [c18]Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré:
Clifford-Steerable Convolutional Neural Networks. ICML 2024 - [i34]Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams
, Johannes Brandstetter:
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics. CoRR abs/2402.06275 (2024) - [i33]Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter:
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations. CoRR abs/2402.10093 (2024) - [i32]Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter:
Universal Physics Transformers. CoRR abs/2402.12365 (2024) - [i31]Arturs Berzins, Andreas Radler, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter:
Geometry-Informed Neural Networks. CoRR abs/2402.14009 (2024) - [i30]Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré:
Clifford-Steerable Convolutional Neural Networks. CoRR abs/2402.14730 (2024) - [i29]Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr:
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. CoRR abs/2403.04747 (2024) - [i28]Artur P. Toshev, Harish Ramachandran, Jonas A. Erbesdobler, Gianluca Galletti, Johannes Brandstetter, Nikolaus A. Adams:
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework. CoRR abs/2403.04750 (2024) - [i27]Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter, Sepp Hochreiter, Andreas Mayr, Günter Klambauer:
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. CoRR abs/2404.07194 (2024) - [i26]Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter:
xLSTM: Extended Long Short-Term Memory. CoRR abs/2405.04517 (2024) - [i25]Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris:
Aurora: A Foundation Model of the Atmosphere. CoRR abs/2405.13063 (2024) - [i24]Benedikt Alkin, Maximilian Beck, Korbinian Pöppel, Sepp Hochreiter, Johannes Brandstetter:
Vision-LSTM: xLSTM as Generic Vision Backbone. CoRR abs/2406.04303 (2024) - 2023
- [j2]Jayesh K. Gupta, Johannes Brandstetter:
Towards Multi-spatiotemporal-scale Generalized PDE Modeling. Trans. Mach. Learn. Res. 2023 (2023) - [c17]Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter:
Boundary Graph Neural Networks for 3D Simulations. AAAI 2023: 9099-9107 - [c16]Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta:
Clifford Neural Layers for PDE Modeling. ICLR 2023 - [c15]Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover:
ClimaX: A foundation model for weather and climate. ICML 2023: 25904-25938 - [c14]David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter:
Geometric Clifford Algebra Networks. ICML 2023: 29306-29337 - [c13]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics-Informed Networks. NeurIPS 2023 - [c12]Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS 2023 - [c11]David Ruhe, Johannes Brandstetter, Patrick Forré:
Clifford Group Equivariant Neural Networks. NeurIPS 2023 - [i23]Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover:
ClimaX: A foundation model for weather and climate. CoRR abs/2301.10343 (2023) - [i22]David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter:
Geometric Clifford Algebra Networks. CoRR abs/2302.06594 (2023) - [i21]Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter, Sepp Hochreiter:
G-Signatures: Global Graph Propagation With Randomized Signatures. CoRR abs/2302.08811 (2023) - [i20]Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams:
E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics. CoRR abs/2304.00150 (2023) - [i19]David Ruhe, Johannes Brandstetter, Patrick Forré:
Clifford Group Equivariant Neural Networks. CoRR abs/2305.11141 (2023) - [i18]Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams:
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks. CoRR abs/2305.15603 (2023) - [i17]Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR abs/2308.05732 (2023) - [i16]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics Informed Networks. CoRR abs/2311.04293 (2023) - 2022
- [c10]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:
Few-Shot Learning by Dimensionality Reduction in Gradient Space. CoLLAs 2022: 1043-1064 - [c9]Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling:
Geometric and Physical Quantities improve E(3) Equivariant Message Passing. ICLR 2022 - [c8]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. ICLR 2022 - [c7]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. ICML 2022: 2241-2256 - [c6]Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, José Antonio Arjona-Medina, Sepp Hochreiter:
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. ICML 2022: 17531-17572 - [i15]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. CoRR abs/2202.03376 (2022) - [i14]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. CoRR abs/2202.07643 (2022) - [i13]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:
Few-Shot Learning by Dimensionality Reduction in Gradient Space. CoRR abs/2206.03483 (2022) - [i12]Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta:
Clifford Neural Layers for PDE Modeling. CoRR abs/2209.04934 (2022) - [i11]Jayesh K. Gupta, Johannes Brandstetter:
Towards Multi-spatiotemporal-scale Generalized PDE Modeling. CoRR abs/2209.15616 (2022) - 2021
- [j1]Markus Holzleitner, Lukas Gruber, José Antonio Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter:
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. Trans. Large Scale Data Knowl. Centered Syst. 48: 105-130 (2021) - [c5]Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami, Nikolaus A. Adams:
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks. GSI (2) 2021: 332-341 - [c4]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:
Hopfield Networks is All You Need. ICLR 2021 - [i10]Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter:
Learning 3D Granular Flow Simulations. CoRR abs/2105.01636 (2021) - [i9]Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter:
Boundary Graph Neural Networks for 3D Simulations. CoRR abs/2106.11299 (2021) - [i8]Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling:
Geometric and Physical Quantities improve E(3) Equivariant Message Passing. CoRR abs/2110.02905 (2021) - 2020
- [c3]Marius-Constantin Dinu, Markus Hofmarcher, Vihang Prakash Patil, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter:
XAI and Strategy Extraction via Reward Redistribution. xxAI@ICML 2020: 177-205 - [c2]Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer:
Modern Hopfield Networks and Attention for Immune Repertoire Classification. NeurIPS 2020 - [i7]Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlovic, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer:
Modern Hopfield Networks and Attention for Immune Repertoire Classification. CoRR abs/2007.13505 (2020) - [i6]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:
Hopfield Networks is All You Need. CoRR abs/2008.02217 (2020) - [i5]Vihang Prakash Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter:
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. CoRR abs/2009.14108 (2020) - [i4]Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David P. Kreil, Michael Kopp, Günter Klambauer, Sepp Hochreiter:
Cross-Domain Few-Shot Learning by Representation Fusion. CoRR abs/2010.06498 (2020) - [i3]Markus Holzleitner, Lukas Gruber, Jose A. Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter:
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. CoRR abs/2012.01399 (2020)
2010 – 2019
- 2019
- [c1]Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter:
RUDDER: Return Decomposition for Delayed Rewards. NeurIPS 2019: 13544-13555 - [i2]Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter:
Quantum Optical Experiments Modeled by Long Short-Term Memory. CoRR abs/1910.13804 (2019) - [i1]Frederik Kratzert, Daniel Klotz, Johannes Brandstetter, Pieter-Jan Hoedt, Grey Nearing, Sepp Hochreiter:
Using LSTMs for climate change assessment studies on droughts and floods. CoRR abs/1911.03941 (2019)
Coauthor Index
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last updated on 2024-09-04 01:20 CEST by the dblp team
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