Hartmut Maennel

13 papers A* 3Journal 10

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties. CoRR Hartmut Maennel, Oliver T. Unke, Klaus-Robert Müller
2024	J	`jnl`	E3x: E(3)-Equivariant Deep Learning Made Easy. CoRR Oliver T. Unke, Hartmut Maennel
2022	J	`jnl`	Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations. CoRR Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller
2021	A*	`conf`	Deep Learning Through the Lens of Example Difficulty. NeurIPS Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
2021	J	`jnl`	Deep Learning Through the Lens of Example Difficulty. CoRR Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
2021	J	`jnl`	The Impact of Reinitialization on Generalization in Convolutional Neural Networks. CoRR Ibrahim M. Alabdulmohsin, Hartmut Maennel, Daniel Keysers
2020	J	`jnl`	Exact marginal inference in Latent Dirichlet Allocation. CoRR Hartmut Maennel
2020	A*	`conf`	What Do Neural Networks Learn When Trained With Random Labels? NeurIPS Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2020	J	`jnl`	What Do Neural Networks Learn When Trained With Random Labels? CoRR Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2019	A*	`conf`	Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. NeurIPS Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu
2019	J	`jnl`	Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. CoRR Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy A. Mann, André Barreto, Sylvain Gelly, Gergely Neu
2018	J	`jnl`	Gradient Descent Quantizes ReLU Network Features. CoRR Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. CoRR Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto