Maria Bånkestad

13 papers Journal 11Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Observation-dependent Bayesian active learning via input-warped Gaussian processes.
CoRR
Sanna Jarl, Maria Bånkestad, Jonathan J. S. Scragg, Jens Sjölund
2025 — —
Structured models for scientific machine learning: From graphs to kernels
Maria Bånkestad
2024 J jnl
Flexible SE(2) graph neural networks with applications to PDE surrogates.
CoRR
Maria Bånkestad, Olof Mogren, Aleksis Pirinen
2024 — conf
Ising on the Graph: Task-Specific Graph Subsampling via the Ising Model.
LoG
Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair, Jens Sjölund
2024 J jnl
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model.
CoRR
Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair, Jens Sjölund
2023 J jnl
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks.
CoRR
Maria Bånkestad, Keven M. Dorst, Göran Widmalm, Jerk Rönnols
2023 J jnl
Variational Elliptical Processes.
CoRR
Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön
2023 J jnl
Variational Elliptical Processes.
Trans. Mach. Learn. Res.
Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön
2022 J jnl
Graph-based Neural Acceleration for Nonnegative Matrix Factorization.
CoRR
Jens Sjölund, Maria Bånkestad
2022 J jnl
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction.
CoRR
Johan Broberg, Maria Bånkestad, Erik Ylipää
2020 J jnl
The Elliptical Processes: a New Family of Flexible Stochastic Processes.
CoRR
Maria Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön
2019 J jnl
Constructing the Matrix Multilayer Perceptron and its Application to the VAE.
CoRR
Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön
2018 J jnl
Bayesian uncertainty quantification in linear models for diffusion MRI.
NeuroImage
Jens Sjölund, Anders Eklund, Evren Özarslan, Magnus Herberthson, Maria Bånkestad, Hans Knutsson