T. Konstantin Rusch 个人学术档案

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引用	619	619
h 指数	10	10
i10 指数	11	11

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Siddhartha MishraProfessor of Applied Mathematics, ETH Zurich, Switzerland在 sam.math.ethz.ch 的电子邮件经过验证
Michael BronsteinDeepMind Professor of AI, University of Oxford在 cs.ox.ac.uk 的电子邮件经过验证
Benjamin Paul ChamberlainIsomorphic Labs在 google.com 的电子邮件经过验证
Michael MahoneyProfessor of Statistics, UC Berkeley在 stat.berkeley.edu 的电子邮件经过验证
Francesco Di GiovanniSenior Research Scientist, Valence Labs @ Recursion Pharma在 valencelabs.com 的电子邮件经过验证
James RowbottomUniversity of Cambridge在 cam.ac.uk 的电子邮件经过验证
Petar VeličkovićStaff Research Scientist, Google DeepMind | Affiliated Lecturer, University of Cambridge在 google.com 的电子邮件经过验证
Andreea DeacIsomorphic Labs在 google.com 的电子邮件经过验证
N. Benjamin ErichsonAssistant Professor在 pitt.edu 的电子邮件经过验证
Christoph SchwabProfessor for Mathematics, ETH Zurich在 math.ethz.ch 的电子邮件经过验证
Léonard EquerETH Zürich在 alumni.ethz.ch 的电子邮件经过验证
Samuel LanthalerCaltech在 caltech.edu 的电子邮件经过验证
Daniela RusAndrew (1956) and Erna Viterbi Professor of Computer Science, MIT在 csail.mit.edu 的电子邮件经过验证
Christiane LemieuxStatistics and Actuarial Science, University of Waterloo在 uwaterloo.ca 的电子邮件经过验证
Nathan KirkUniversity of Waterloo在 uwaterloo.ca 的电子邮件经过验证

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T. Konstantin Rusch

MIT

在 mit.edu 的电子邮件经过验证 - 首页

Machine Learning Deep Learning Computational Science Applied Mathematics


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
A survey on oversmoothing in graph neural networks TK Rusch, MM Bronstein, S Mishra arXiv preprint arXiv:2303.10993, 2023	142	2023
Graph-coupled oscillator networks TK Rusch, B Chamberlain, J Rowbottom, S Mishra, M Bronstein International Conference on Machine Learning, 18888-18909, 2022	100	2022
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies TK Rusch, S Mishra 9th International Conference on Learning Representations (ICLR), 2021	91	2021
Unicornn: A recurrent model for learning very long time dependencies TK Rusch, S Mishra International Conference on Machine Learning, 9168-9178, 2021	69	2021
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences S Mishra, TK Rusch SIAM Journal on Numerical Analysis 59 (3), 1811-1834, 2021	61	2021
Long Expressive Memory for Sequence Modeling TK Rusch, S Mishra, NB Erichson, MW Mahoney 10th International Conference on Learning Representations (ICLR), 2022	42	2022
Gradient Gating for Deep Multi-Rate Learning on Graphs TK Rusch, BP Chamberlain, MW Mahoney, MM Bronstein, S Mishra 11th International Conference on Learning Representations (ICLR), 2023	38	2023
How does over-squashing affect the power of GNNs? F Di Giovanni, TK Rusch, MM Bronstein, A Deac, M Lackenby, S Mishra, ... Transactions on Machine Learning Research, 2024	25*	2024
Higher-order quasi-Monte Carlo training of deep neural networks M Longo, S Mishra, TK Rusch, C Schwab SIAM Journal on Scientific Computing 43 (6), A3938-A3966, 2021	21	2021
A survey on oversmoothing in graph neural networks. arXiv TK Rusch, MM Bronstein, S Mishra arXiv preprint arXiv:2303.10993, 2023	11*	2023
Multi-Scale Message Passing Neural PDE Solvers L Equer, TK Rusch, S Mishra ICLR 2023 Workshop on Physics for Machine Learning, 2023	10	2023
Neural oscillators are universal S Lanthaler, TK Rusch, S Mishra Advances in Neural Information Processing Systems 36, 2024	8	2024
Reproducing Existing Nacelle Geometries With the Free-Form Deformation Parametrization K Rusch, M Siggel, RG Becker Turbo Expo: Power for Land, Sea, and Air 51029, V02DT46A015, 2018	1	2018
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks TK Rusch, N Kirk, MM Bronstein, C Lemieux, D Rus arXiv preprint arXiv:2405.15059, 2024		2024
Physics-inspired Machine Learning TK Rusch ETH Zurich, 2023		2023

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