4 citations · 8 across the 4 of their papers we have counts for
5 papers
Observation-dependent Bayesian active learning via input-warped Gaussian processes
Sanna Jarl, Maria Bånkestad, Jonathan J. S. Scragg +1
Bayesian active learning relies on the precise quantification of predictive uncertainty to explore unknown function landscapes. While Gaussian process surrogates are the standard f…
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…
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…