| Unveiling the strong interaction among hadrons at the LHC Nature 588 (7837), 232-238, 2020 | 204 | 2020 |
| Generative models for fast cluster simulations in the TPC for the ALICE experiment K Deja, T Trzcinski, L Graczykowski Proceedings, 23rd International Conference on Computing in High Energy and …, 2019 | 39 | 2019 |
| On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models K Deja, A Kuzina, T Trzciński, JM Tomczak NeurIPS 2022, 2022 | 33 | 2022 |
| Learning data representations with joint diffusion models K Deja, T Trzciński, JM Tomczak Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 18 | 2023 |
| End-to-end sinkhorn autoencoder with noise generator K Deja, J Dubiński, P Nowak, S Wenzel, P Spurek, T Trzcinski IEEE Access 9, 7211-7219, 2020 | 16 | 2020 |
| Exploring continual learning of diffusion models M Zając, K Deja, A Kuzina, JM Tomczak, T Trzciński, F Shkurti, P Miłoś arXiv preprint arXiv:2303.15342, 2023 | 15 | 2023 |
| Using machine learning for particle identification in ALICE ŁK Graczykowski, M Jakubowska, KR Deja, M Kabus, Alice Collaboration Journal of Instrumentation 17 (07), C07016, 2022 | 14 | 2022 |
| Binplay: A binary latent autoencoder for generative replay continual learning K Deja, P Wawrzyński, D Marczak, W Masarczyk, T Trzciński 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 11 | 2021 |
| Machine learning methods for simulating particle response in the zero degree calorimeter at the ALICE experiment, CERN J Dubiński, K Deja, S Wenzel, P Rokita, T Trzciński AIP Conference Proceedings 3061 (1), 2024 | 10 | 2024 |
| Looking through the past: better knowledge retention for generative replay in continual learning V Khan, S Cygert, B Twardowski, T Trzciński Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 10 | 2023 |
| Using Machine Learning techniques for Data Quality Monitoring in CMS and ALICE experiments KR Deja PoS, 236, 2019 | 10 | 2019 |
| Automatic evaluation of speaker similarity K Deja, A Sanchez, J Roth, M Cotescu Interspeech 2022, 2022 | 8* | 2022 |
| Assigning quality labels in the high-energy physics experiment ALICE using machine learning algorithms T Trzcinski, K Deja Acta Phys. Polon. Suppl. A 11, 647, 2018 | 7 | 2018 |
| Machine-learning-based particle identification with missing data M Kasak, K Deja, M Karwowska, M Jakubowska, Ł Graczykowski, M Janik The European Physical Journal C 84 (7), 691, 2024 | 6 | 2024 |
| Guide: Guidance-based incremental learning with diffusion models B Cywiński, K Deja, T Trzciński, B Twardowski, Ł Kuciński arXiv preprint arXiv:2403.03938, 2024 | 5 | 2024 |
| Selectively increasing the diversity of gan-generated samples J Dubiński, K Deja, S Wenzel, P Rokita, T Trzcinski International Conference on Neural Information Processing, 260-270, 2022 | 5 | 2022 |
| Logarithmic continual learning W Masarczyk, P Wawrzyński, D Marczak, K Deja, T Trzciński IEEE Access 10, 117001-117010, 2022 | 5 | 2022 |
| On robustness of generative representations against catastrophic forgetting W Masarczyk, K Deja, T Trzcinski International Conference on Neural Information Processing, 325-333, 2021 | 5 | 2021 |
| Multiband VAE: latent space alignment for knowledge consolidation in continual learning K Deja, P Wawrzynski, W Masarczyk, D Marczak, T Trzciński IJCAI 2022, 2022 | 4 | 2022 |
| Generative diffusion models for fast simulations of particle collisions at cern M Kita, J Dubiński, P Rokita, K Deja arXiv preprint arXiv:2406.03233, 2024 | 3 | 2024 |
Tomasz TrzcinskiWarsaw University of Technology, Tooploox, IDEAS NCBRVerified email at pw.edu.pl
