21 citations · 32 across the 11 of their papers we have counts for
12 papers
Understanding Hidden Spatial Preferences in Bayesian Optimization
Maria Bånkestad, Jens Sjölund
Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian-process posterior variance…
Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
Maria Bånkestad, Sanna Jarl, Jens Sjölund
Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized artifact in geostatistics and a…
A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles
Maria Bånkestad, Sandra Barman, Magnus Röding +11
Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray…
Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
Sanna Jarl, Maria Bånkestad, Jonathan J. S. Scragg +1
In many experimental settings, active learning can improve sample efficiency by sequentially selecting where to measure, which is particularly valuable when experiments are expensi…
Flexible SE(2) graph neural networks with applications to PDE surrogates
Maria Bånkestad, Olof Mogren, Aleksis Pirinen
This paper presents a novel approach for constructing graph neural networks equivariant to 2D rotations and translations and leveraging them as PDE surrogates on non-gridded domain…
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair +1
Reducing a graph while preserving its overall properties is an important problem with many applications. Typically, reduction approaches either remove edges (sparsification) or mer…
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks
Maria Bånkestad, Keven M. Dorst, Göran Widmalm +1
Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understa…
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction
Johan Broberg, Maria Bånkestad, Erik Ylipää
Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the tr…
Graph-based Neural Acceleration for Nonnegative Matrix Factorization
Jens Sjölund, Maria Bånkestad
We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known…
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia +1
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process. This generalization includes a…
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Jalil Taghia, Maria Bånkestad, Fredrik Lindsten +1
Like most learning algorithms, the multilayer perceptrons (MLP) is designed to learn a vector of parameters from data. However, in certain scenarios we are interested in learning s…
Bayesian uncertainty quantification in linear models for diffusion MRI
Jens Sjölund, Anders Eklund, Evren Özarslan +3
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Se…