Bayesian uncertainty quantification in linear models for diffusion MRI. - Jens Sjölund; Anders Eklund 002; Evren Özarslan; Magnus Herberthson; Maria Bånkestad; Hans Knutsson (NeuroImage, 2018)

Constructing the Matrix Multilayer Perceptron and its Application to the VAE. - Jalil Taghia; Maria Bånkestad; Fredrik Lindsten; Thomas B. Schön (CoRR, 2019)

The Elliptical Processes: a New Family of Flexible Stochastic Processes. - Maria Bånkestad; Jens Sjölund; Jalil Taghia; Thomas B. Schön (CoRR, 2020)

Graph-based Neural Acceleration for Nonnegative Matrix Factorization. - Jens Sjölund; Maria Bånkestad (CoRR, 2022)

Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction. - Johan Broberg; Maria Bånkestad; Erik Ylipää (CoRR, 2022)

Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks. - Maria Bånkestad; Keven M. Dorst; Göran Widmalm; Jerk Rönnols (CoRR, 2023)

Variational Elliptical Processes. - Maria Bånkestad; Jens Sjölund; Jalil Taghia; Thomas B. Schön (Trans. Mach. Learn. Res., 2023)

Ising on the Graph: Task-Specific Graph Subsampling via the Ising Model. - Maria Bånkestad; Jennifer R. Andersson; Sebastian Mair 001; Jens Sjölund (LoG, 2024)

Flexible SE(2) graph neural networks with applications to PDE surrogates. - Maria Bånkestad; Olof Mogren; Aleksis Pirinen (CoRR, 2024)

Structured models for scientific machine learning: From graphs to kernels - Maria Bånkestad (2025)