2.8k citations · 19.2k across the 71 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
q-bio.NC2021★ 3 cited
Evaluating deep transfer learning for whole-brain cognitive decoding
Armin W. Thomas, Ulman Lindenberger, Wojciech Samek +1
Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This…
physics.chem-ph2021★ 347 cited
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger +3
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electroni…