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2020 – today
- 2024
[i9]Ziming Wang, Rebecka Jörnsten:
SE(3)-bi-equivariant Transformers for Point Cloud Assembly. CoRR abs/2407.09167 (2024)- [i8]Zi-Ming Wang, Nan Xue, Ling Lei, Rebecka Jörnsten, Gui-Song Xia:
Partial Distribution Matching via Partial Wasserstein Adversarial Networks. CoRR abs/2409.10499 (2024) - 2023
- [j9]David Martínez-Enguita
, Sanjiv K. Dwivedi, Rebecka Jörnsten, Mika Gustafsson:
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures. Briefings Bioinform. 24(5) (2023) - [j8]Oskar Allerbo, Johan Jonasson, Rebecka Jörnsten:
Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net. J. Mach. Learn. Res. 24: 277:1-277:53 (2023) - [i7]Viktor Andersson, Balázs Varga, Vincent Szolnoky, Andreas Syrén, Rebecka Jörnsten, Balázs Kulcsár:
Controlled Descent Training. CoRR abs/2303.09216 (2023) - [i6]Oskar Allerbo, Rebecka Jörnsten:
Solving Kernel Ridge Regression with Gradient-Based Optimization Methods. CoRR abs/2306.16838 (2023) - 2022
- [j7]Oskar Allerbo, Rebecka Jörnsten:
Flexible, non-parametric modeling using regularized neural networks. Comput. Stat. 37(4): 2029-2047 (2022) - [c3]Vincent Szolnoky, Viktor Andersson, Balázs Kulcsár, Rebecka Jörnsten:
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity. NeurIPS 2022 - [i5]Oskar Allerbo, Rebecka Jörnsten:
Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control. CoRR abs/2205.11956 (2022) - [i4]Vincent Szolnoky, Viktor Andersson, Balázs Kulcsár, Rebecka Jörnsten:
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity. CoRR abs/2205.12642 (2022) - 2021
- [j6]Oskar Allerbo, Rebecka Jörnsten:
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders. J. Mach. Learn. Res. 22: 283:1-283:28 (2021) - [i3]Oskar Allerbo, Rebecka Jörnsten:
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders. CoRR abs/2102.10873 (2021) - [i2]Olof Zetterqvist, Rebecka Jörnsten, Johan Jonasson:
Robust Neural Network Classification via Double Regularization. CoRR abs/2112.08102 (2021) - 2020
- [i1]Oskar Allerbo, Rebecka Jörnsten:
Flexible, Non-parametric Modeling Using Regularized Neural Networks. CoRR abs/2012.11369 (2020)
2010 – 2019
- 2017
- [j5]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, Gunnar Cedersund, Mikael Benson, Andreas Tjärnberg, Mika Gustafsson:
LASSIM - A network inference toolbox for genome-wide mechanistic modeling. PLoS Comput. Biol. 13(6) (2017)
2000 – 2009
- 2007
- [j4]Rebecka Jörnsten, Ming Ouyang, Hui-Yu Wang:
A meta-data based method for DNA microarray imputation. BMC Bioinform. 8 (2007) - 2005
- [j3]Rebecka Jörnsten, Hui-Yu Wang, William J. Welsh, Ming Ouyang:
DNA microarray data imputation and significance analysis of differential expression. Bioinform. 21(22): 4155-4161 (2005) - 2003
- [j2]Rebecka Jörnsten, Bin Yu:
Simultaneous Gene Clustering and Subset Selection for Sample Classification Via MDL. Bioinform. 19(9): 1100-1109 (2003) - [j1]Rebecka Jörnsten, Wei Wang, Bin Yu, Kannan Ramchandran:
Microarray image compression: SLOCO and the effect of information loss. Signal Process. 83(4): 859-869 (2003) - 2002
- [c2]Rebecka Jörnsten, Bin Yu, Wei Wang, Kannan Ramchandran:
Compression of cDNA and inkjet microarray images. ICIP (3) 2002: 961-964 - [c1]Rebecka Jörnsten, Bin Yu:
Compression of cDNA microarray images. ISBI 2002: 38-41
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