14 citations · 24 across the 6 of their papers we have counts for
6 papers
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…
Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To da…
Explainable Image Quality Assessments in Teledermatological Photography
Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
Image quality is a crucial factor in the effectiveness and efficiency of teledermatological consultations. However, up to 50% of images sent by patients have quality issues, thus i…
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge +2
Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due…
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga +3
Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagno…
Reconstructing the exit wave in high-resolution transmission electron microscopy using machine learning
Matthew Helmi Leth Larsen, Frederik Dahl, Lars P. Hansen +6
Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolution…