84 citations · 325 across the 22 of their papers we have counts for
4 papers · 1 filter
Coherent energy and force uncertainty in deep learning force fields
Peter Bjørn Jørgensen, Jonas Busk, Ole Winther +1
In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quanti…
Multi-view self-supervised learning for multivariate variable-channel time series
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets thro…
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
Bo Li, Yasin Esfandiari, Mikkel N. Schmidt +2
In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may viol…
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther +2
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting an…