| Space–time tradeoffs for subset sum: An improved worst case algorithm P Austrin, P Kaski, M Koivisto, J Määttä International Colloquium on Automata, Languages, and Programming, 45-56, 2013 | 28 | 2013 |
| Gradient-based training and pruning of radial basis function networks with an application in materials physics J Määttä, V Bazaliy, J Kimari, F Djurabekova, K Nordlund, T Roos Neural Networks 133, 123-131, 2021 | 14 | 2021 |
| Subset selection in linear regression using sequentially normalized least squares: Asymptotic theory J Määttä, DF Schmidt, T Roos Scandinavian Journal of Statistics 43 (2), 382-395, 2016 | 14 | 2016 |
| A fixed-point image denoising algorithm with automatic window selection J Määttä, S Siltanen, T Roos 2014 5th European Workshop on Visual Information Processing (EUVIP), 1-6, 2014 | 7* | 2014 |
| Maximum parsimony and the skewness test: A simulation study of the limits of applicability J Määttä, T Roos Plos one 11 (4), e0152656, 2016 | 3 | 2016 |
| Robust Sequential Prediction in Linear Regression with Student's t-distribution. J Määttä, T Roos ISAIM, 2016 | 2 | 2016 |
| Model Selection Methods for Linear Regression and Phylogenetic Reconstruction J Määttä Helsingin yliopisto, 2016 | 1 | 2016 |
| Supporting Information for “Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory”: Appendix S1: Proofs of Lemmas J Määttä, DF Schmidt, T Roos | | |
