20 citations · 44 across the 4 of their papers we have counts for
8 papers
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…