Hartmut Maennel | DeepAI

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∙ 05/17/2022

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Molecular dynamics (MD) simulations allow atomistic insights into chemic...

11 Oliver T. Unke, et al. ∙

∙ 09/01/2021

The Impact of Reinitialization on Generalization in Convolutional Neural Networks

Recent results suggest that reinitializing a subset of the parameters of...

0 Ibrahim Alabdulmohsin, et al. ∙

∙ 06/17/2021

Deep Learning Through the Lens of Example Difficulty

Existing work on understanding deep learning often employs measures that...

0 Robert J. N. Baldock, et al. ∙

∙ 06/18/2020

What Do Neural Networks Learn When Trained With Random Labels?

We study deep neural networks (DNNs) trained on natural image data with ...

0 Hartmut Maennel, et al. ∙

∙ 03/31/2020

Exact marginal inference in Latent Dirichlet Allocation

Assume we have potential "causes" z∈ Z, which produce "events" w with kn...

0 Hartmut Maennel, et al. ∙

∙ 06/19/2019

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

We consider the core reinforcement-learning problem of on-policy value f...

1 Hugo Penedones, et al. ∙

∙ 07/09/2018

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

Temporal-Difference learning (TD) [Sutton, 1988] with function approxima...

0 Hugo Penedones, et al. ∙

∙ 03/22/2018

Gradient Descent Quantizes ReLU Network Features

Deep neural networks are often trained in the over-parametrized regime (...

0 Hartmut Maennel, et al. ∙

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