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ORCIDT. Konstantin Rusch
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2020 – today
- 2024
[j3]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024)- [i12]T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus:
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks. CoRR abs/2405.15059 (2024) - 2023
- [c6]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR 2023 - [c5]Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra:
Neural Oscillators are Universal. NeurIPS 2023 - [i11]Léonard Equer, T. Konstantin Rusch, Siddhartha Mishra:
Multi-Scale Message Passing Neural PDE Solvers. CoRR abs/2302.03580 (2023) - [i10]T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra:
A Survey on Oversmoothing in Graph Neural Networks. CoRR abs/2303.10993 (2023) - [i9]Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra:
Neural Oscillators are Universal. CoRR abs/2305.08753 (2023) - [i8]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? CoRR abs/2306.03589 (2023) - 2022
- [c4]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. ICLR 2022 - [c3]T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. ICML 2022: 18888-18909 - [i7]T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein:
Graph-Coupled Oscillator Networks. CoRR abs/2202.02296 (2022) - [i6]T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra:
Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR abs/2210.00513 (2022) - 2021
- [j2]Siddhartha Mishra, T. Konstantin Rusch
:
Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences. SIAM J. Numer. Anal. 59(3): 1811-1834 (2021) - [j1]Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab:
Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks. SIAM J. Sci. Comput. 43(6): A3938-A3966 (2021) - [c2]T. Konstantin Rusch, Siddhartha Mishra:
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. ICLR 2021 - [c1]T. Konstantin Rusch, Siddhartha Mishra:
UnICORNN: A recurrent model for learning very long time dependencies. ICML 2021: 9168-9178 - [i5]T. Konstantin Rusch, Siddhartha Mishra:
UnICORNN: A recurrent model for learning very long time dependencies. CoRR abs/2103.05487 (2021) - [i4]T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney:
Long Expressive Memory for Sequence Modeling. CoRR abs/2110.04744 (2021) - 2020
- [i3]Siddhartha Mishra, T. Konstantin Rusch:
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences. CoRR abs/2005.12564 (2020) - [i2]Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab:
Higher-order Quasi-Monte Carlo Training of Deep Neural Networks. CoRR abs/2009.02713 (2020) - [i1]T. Konstantin Rusch, Siddhartha Mishra:
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. CoRR abs/2010.00951 (2020)
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