Search Results - "Bånkestad, Maria"

hERG-Toxicity Prediction using Traditional Machine Learning and Advanced Deep Learning Techniques by Ylipää, Erik, Chavan, Swapnil, Bånkestad, Maria, Broberg, Johan, Glinghammar, Björn, Norinder, Ulf, Cotgreave, Ian

“…[Display omitted] •Robust AI/ML models for the largest hERG dataset to date.•Benchmarking of advanced deep learning techniques against traditional…”
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Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks by Bånkestad, Maria, Dorst, Kevin M, Widmalm, Göran, Rönnols, Jerk

“…Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a…”
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Bayesian uncertainty quantification in linear models for diffusion MRI by Sjölund, Jens, Eklund, Anders, Özarslan, Evren, Herberthson, Magnus, Bånkestad, Maria, Knutsson, Hans

“…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…”
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Graph-based Neural Acceleration for Nonnegative Matrix Factorization by Sjölund, Jens, Bånkestad, Maria

“…We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite…”
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Flexible SE(2) graph neural networks with applications to PDE surrogates by Bånkestad, Maria, Mogren, Olof, Pirinen, Aleksis

“…This paper presents a novel approach for constructing graph neural networks equivariant to 2D rotations and translations and leveraging them as PDE surrogates…”
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Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction by Broberg, Johan, Bånkestad, Maria, Ylipää, Erik

“…Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often…”
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Ising on the Graph: Task-specific Graph Subsampling via the Ising Model by Bånkestad, Maria, Andersson, Jennifer R, Mair, Sebastian, Sjölund, Jens

“…Reducing a graph while preserving its overall structure is an important problem with many applications. Typically, reduction approaches either remove edges…”
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Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks by Bånkestad, Maria, Dorst, Keven M, Widmalm, Göran, Rönnols, Jerk

“…Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a…”
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Variational Elliptical Processes by Bånkestad, Maria, Sjölund, Jens, Taghia, Jalil, Schöon, Thomas B

“…Transactions on Machine Learning Research, September 2023 We present elliptical processes, a family of non-parametric probabilistic models that subsume…”
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The Elliptical Processes: a Family of Fat-tailed Stochastic Processes by Bånkestad, Maria, Sjölund, Jens, Taghia, Jalil, Schön, Thomas

“…We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process. This…”
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Constructing the Matrix Multilayer Perceptron and its Application to the VAE by Taghia, Jalil, Bånkestad, Maria, Lindsten, Fredrik, Schön, Thomas B

“…Like most learning algorithms, the multilayer perceptrons (MLP) is designed to learn a vector of parameters from data. However, in certain scenarios we are…”
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Bayesian uncertainty quantification in linear models for diffusion MRI by Sjölund, Jens, Eklund, Anders, Özarslan, Evren, Herberthson, Magnus, Bånkestad, Maria, Knutsson, Hans

“…NeuroImage, 2018; 175:272-285 Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is…”
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