Compression of cDNA and inkjet microarray images. - Rebecka Jörnsten; Bin Yu 001; Wei Wang 039; Kannan Ramchandran (ICIP (3), 2002)

Compression of cDNA microarray images. - Rebecka Jörnsten; Bin Yu 001 (ISBI, 2002)

Simultaneous Gene Clustering and Subset Selection for Sample Classification Via MDL. - Rebecka Jörnsten; Bin Yu 001 (Bioinform., 2003)

Microarray image compression: SLOCO and the effect of information loss. - Rebecka Jörnsten; Wei Wang 039; Bin Yu 001; Kannan Ramchandran (Signal Process., 2003)

DNA microarray data imputation and significance analysis of differential expression. - Rebecka Jörnsten; Hui-Yu Wang; William J. Welsh; Ming Ouyang (Bioinform., 2005)

A meta-data based method for DNA microarray imputation. - Rebecka Jörnsten; Ming Ouyang; Hui-Yu Wang (BMC Bioinform., 2007)

LASSIM - A network inference toolbox for genome-wide mechanistic modeling. - Rasmus Magnusson; Guido Pio Mariotti; Mattias Köpsén; William Lövfors; Danuta R. Gawel; Rebecka Jörnsten; Jörg Linde; Torbjörn E. M. Nordling; Elin Nyman; Sylvie Schulze; Colm E. Nestor; Huan Zhang; et al. (PLoS Comput. Biol., 2017)

Robust Neural Network Classification via Double Regularization. - Olof Zetterqvist; Rebecka Jörnsten; Johan Jonasson (CoRR, 2021)

Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders. - Oskar Allerbo; Rebecka Jörnsten (J. Mach. Learn. Res., 2021)

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity. - Vincent Szolnoky; Viktor Andersson; Balázs Kulcsár; Rebecka Jörnsten (NeurIPS, 2022)

Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control. - Oskar Allerbo; Rebecka Jörnsten (CoRR, 2022)

Flexible, non-parametric modeling using regularized neural networks. - Oskar Allerbo; Rebecka Jörnsten (Comput. Stat., 2022)

Controlled Descent Training. - Viktor Andersson; Balázs Varga; Vincent Szolnoky; Andreas Syrén; Rebecka Jörnsten; Balázs Kulcsár (CoRR, 2023)

Solving Kernel Ridge Regression with Gradient-Based Optimization Methods. - Oskar Allerbo; Rebecka Jörnsten (CoRR, 2023)

Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net. - Oskar Allerbo; Johan Jonasson; Rebecka Jörnsten (J. Mach. Learn. Res., 2023)

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures. - David Martínez-Enguita; Sanjiv K. Dwivedi; Rebecka Jörnsten; Mika Gustafsson (Briefings Bioinform., 2023)

SE(3)-bi-equivariant Transformers for Point Cloud Assembly. - Ziming Wang; Rebecka Jörnsten (NeurIPS, 2024)

Equivariant Flow Matching for Point Cloud Assembly. - Ziming Wang; Nan Xue 006; Rebecka Jörnsten (CoRR, 2025)

Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport. - Jayadev Naram; Fredrik Hellström; Ziming Wang; Rebecka Jörnsten; Giuseppe Durisi (CoRR, 2025)

Partial Distribution Matching via Partial Wasserstein Adversarial Networks. - Zi-Ming Wang; Nan Xue 001; Ling Lei; Rebecka Jörnsten; Gui-Song Xia (IEEE Trans. Pattern Anal. Mach. Intell., 2025)