28 citations · 102 across the 9 of their papers we have counts for
9 papers
E3x: -Equivariant Deep Learning Made Easy
Oliver T. Unke, Hartmut Maennel
This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group , consisting of translations, rota…
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Oliver T. Unke, Martin Stöhr, Stefan Ganscha +8
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical ca…
The Impact of Reinitialization on Generalization in Convolutional Neural Networks
Ibrahim Alabdulmohsin, Hartmut Maennel, Daniel Keysers
Recent results suggest that reinitializing a subset of the parameters of a neural network during training can improve generalization, particularly for small training sets. We study…
Deep Learning Through the Lens of Example Difficulty
Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers. In this work, we adopt a perspective based on t…
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin +4
We study deep neural networks (DNNs) trained on natural image data with entirely random labels. Despite its popularity in the literature, where it is often used to study memorizati…
Exact marginal inference in Latent Dirichlet Allocation
Hartmut Maennel
Assume we have potential "causes" , which produce "events" with known probabilities . We observe , what can we say about the distribution of th…
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos Riquelme, Damien Vincent +5
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (T…
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Hugo Penedones, Damien Vincent, Hartmut Maennel +3
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the sim…
Gradient Descent Quantizes ReLU Network Features
Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solut…