| Distribution-interpolation trade off in generative models D Leśniak, I Sieradzki, I Podolak International Conference on Learning Representations, 2018 | 21 | 2018 |
| Explaining self-supervised image representations with visual probing D Basaj, W Oleszkiewicz, I Sieradzki, M Górszczak, B Rychalska, ... Freiburg, Germany: International Joint Conferences on Artificial Intelligence, 2021 | 17 | 2021 |
| Development of new methods needs proper evaluation—benchmarking sets for machine learning experiments for class a GPCRS D Lesniak, S Podlewska, S Jastrzębski, I Sieradzki, AJ Bojarski, J Tabor Journal of Chemical Information and Modeling 59 (12), 4974-4992, 2019 | 9 | 2019 |
| How sure can we be about ML methods-based evaluation of compound activity: incorporation of information about prediction uncertainty using deep learning techniques I Sieradzki, D Leśniak, S Podlewska Molecules 25 (6), 1452, 2020 | 3 | 2020 |
| Active Learning of Compounds Activity–Towards Scientifically Sound Simulation of Drug Candidates Identification WM Czarnecki, S Jastrzebski, I Sieradzki, S Podlewska Proceedings of MLLS: 2nd Workshop on Machine Learning in Life Sciences, 40-51, 2015 | 3 | 2015 |
| Visual probing: Cognitive framework for explaining self-supervised image representations W Oleszkiewicz, D Basaj, I Sieradzki, M Górszczak, B Rychalska, ... IEEE Access 11, 13028-13043, 2023 | 2 | 2023 |
| Three-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structure S Jastrzębski, I Sieradzki, D Leśniak, J Tabor, AJ Bojarski, S Podlewska Molecular Diversity 23, 603-613, 2019 | 2 | 2019 |
| On certain limitations of recursive representation model S Jastrzębski, I Sieradzki Schedae Informaticae 25, 2016 | | 2016 |
