Hartmut Maennel

According to our database, Hartmut Maennel authored at least 10 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties.

Hartmut Maennel, Oliver T. Unke, Klaus-Robert Müller

CoRR, 2024

E3x: E(3)-Equivariant Deep Learning Made Easy.

Oliver T. Unke, Hartmut Maennel

CoRR, 2024

2022

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations.

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

CoRR, 2022

2021

The Impact of Reinitialization on Generalization in Convolutional Neural Networks.

Ibrahim M. Alabdulmohsin, Hartmut Maennel, Daniel Keysers

CoRR, 2021

Deep Learning Through the Lens of Example Difficulty.

Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Exact marginal inference in Latent Dirichlet Allocation.

Hartmut Maennel

CoRR, 2020

What Do Neural Networks Learn When Trained With Random Labels?

Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates.

Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem.

Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto

CoRR, 2018

Gradient Descent Quantizes ReLU Network Features.

Hartmut Maennel, Olivier Bousquet, Sylvain Gelly

CoRR, 2018