T. Konstantin Rusch

33 papers A* 7Journal 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The Key to State Reduction in Linear Attention: A Rank-based Perspective. CoRR Philipp Nazari, T. Konstantin Rusch
2025	J	`jnl`	Learning to Dissipate Energy in Oscillatory State-Space Models. CoRR Jared Boyer, T. Konstantin Rusch, Daniela Rus
2025	J	`jnl`	Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees. CoRR Maximilian Stölzle, T. Konstantin Rusch, Zach J. Patterson, Rodrigo Pérez-Dattari, Francesco Stella, Josie Hughes, Cosimo Della Santina, Daniela Rus
2025	J	`jnl`	Low Stein Discrepancy via Message-Passing Monte Carlo. CoRR Nathan Kirk, T. Konstantin Rusch, Jakob Zech, Daniela Rus
2025	J	`jnl`	Neural Low-Discrepancy Sequences. CoRR Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine, Daniela Rus, T. Konstantin Rusch
2025	J	`jnl`	On the optimization of discrepancy measures. CoRR François Clément, Nathan Kirk, Art B. Owen, T. Konstantin Rusch
2025	A*	`conf`	Oscillatory State-Space Models. ICLR T. Konstantin Rusch, Daniela Rus
2025	J	`jnl`	Quantifying Memory Use in Reinforcement Learning with Temporal Range. CoRR Rodney S. Lafuente Mercado, Daniela Rus, T. Konstantin Rusch
2025	J	`jnl`	The Curious Case of In-Training Compression of State Space Models. CoRR Makram Chahine, Philipp Nazari, Daniela Rus, T. Konstantin Rusch
2024	J	`jnl`	How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
2024	J	`jnl`	Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo. CoRR Makram Chahine, T. Konstantin Rusch, Zach J. Patterson, Daniela Rus
2024	J	`jnl`	Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks. CoRR T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus
2024	J	`jnl`	Oscillatory State-Space Models. CoRR T. Konstantin Rusch, Daniela Rus
2024	J	`jnl`	Relaxed Equivariance via Multitask Learning. CoRR Ahmed A. A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael M. Bronstein
2023	J	`jnl`	A Survey on Oversmoothing in Graph Neural Networks. CoRR T. Konstantin Rusch, Michael M. Bronstein, Siddhartha Mishra
2023	A*	`conf`	Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2023	J	`jnl`	How does over-squashing affect the power of GNNs? CoRR Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic
2023	J	`jnl`	Multi-Scale Message Passing Neural PDE Solvers. CoRR Léonard Equer, T. Konstantin Rusch, Siddhartha Mishra
2023	A*	`conf`	Neural Oscillators are Universal. NeurIPS Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
2023	J	`jnl`	Neural Oscillators are Universal. CoRR Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
2022	J	`jnl`	Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2022	A*	`conf`	Graph-Coupled Oscillator Networks. ICML T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
2022	J	`jnl`	Graph-Coupled Oscillator Networks. CoRR T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
2022	A*	`conf`	Long Expressive Memory for Sequence Modeling. ICLR T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
2021	A*	`conf`	Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. ICLR T. Konstantin Rusch, Siddhartha Mishra
2021	J	`jnl`	Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences. SIAM J. Numer. Anal. Siddhartha Mishra, T. Konstantin Rusch
2021	J	`jnl`	Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks. SIAM J. Sci. Comput. Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab
2021	J	`jnl`	Long Expressive Memory for Sequence Modeling. CoRR T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney
2021	A*	`conf`	UnICORNN: A recurrent model for learning very long time dependencies. ICML T. Konstantin Rusch, Siddhartha Mishra
2021	J	`jnl`	UnICORNN: A recurrent model for learning very long time dependencies. CoRR T. Konstantin Rusch, Siddhartha Mishra
2020	J	`jnl`	Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies. CoRR T. Konstantin Rusch, Siddhartha Mishra
2020	J	`jnl`	Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences. CoRR Siddhartha Mishra, T. Konstantin Rusch
2020	J	`jnl`	Higher-order Quasi-Monte Carlo Training of Deep Neural Networks. CoRR Marcello Longo, Siddhartha Mishra, T. Konstantin Rusch, Christoph Schwab