Bayesian uncertainty quantification in linear models for diffusion MRI J Sjölund, A Eklund, E Özarslan, M Herberthson, M Bånkestad, ... NeuroImage 175, 272-285, 2018 | 18 | 2018 |
Variational Elliptical Processes M Bånkestad, J Sjölund, J Taghia, TB Schön Transactions on Machine Learning Research, 2023 | 7* | 2023 |
Graph-based neural acceleration for nonnegative matrix factorization J Sjölund, M Bånkestad arXiv preprint arXiv:2202.00264, 2022 | 7 | 2022 |
Pre-training transformers for molecular property prediction using reaction prediction J Broberg, M Bånkestad, E Ylipää AI for Science Workshop: ICML, 2022 | 5 | 2022 |
Modeling, Simulation and Dynamic control of a Wave Energy Converter M Bånkestad | 4 | 2013 |
Constructing the Matrix Multilayer Perceptron and its Application to the VAE J Taghia, M Bånkestad, F Lindsten, TB Schön arXiv preprint arXiv:1902.01182, 2019 | 3 | 2019 |
Carbohydrate NMR chemical shift predictions using E (3) equivariant graph neural networks M Bånkestad, KM Dorst, G Widmalm, J Rönnols arXiv preprint arXiv:2311.12657, 2023 | 2 | 2023 |
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model M Bånkestad, J Andersson, S Mair, J Sjölund arXiv preprint arXiv:2402.10206, 2024 | 1 | 2024 |
hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques E Ylipää, S Chavan, M Bånkestad, J Broberg, B Glinghammar, U Norinder, ... Current Research in Toxicology 5, 100121, 2023 | 1 | 2023 |
Flexible SE (2) graph neural networks with applications to PDE surrogates M Bånkestad, O Mogren, A Pirinen arXiv preprint arXiv:2405.20287, 2024 | | 2024 |
Matrix Multilayer Perceptron J Taghia, M Bånkestad, F Lindsten, T Schön | | |