mgr Dawid Rymarczyk
Jednostki:
- Wydział Matematyki i Informatyki UJ
- Instytut Informatyki i Matematyki Komputerowej
- Katedra Uczenia Maszynowego
Publikacje:
15.
Bartosz Zieliński, Dawid Rymarczyk, Weiwei Schultz, Adriana Borowa, Joshua Friedman, Tomasz Danel, Patrick Branigan, Michał Chałupczak, Anna Bracha, Tomasz Krawiec, Michał Warchoł, Katherine Li, Gert De Hertogh, Louis R. Ghanem, Aleksandar Stojmirovic
14.
Bartosz Zieliński, Łukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging, European Conference on Artificial Intelligence [ECAI], (2023),
13.
Bartosz Zieliński, Adam Pardyl, Dawid Rymarczyk, Joanna Jaworek-Korjakowska, Dariusz Kucharski, Andrzej Brodzicki, Julia Lasek, Zofia Schneider, Iwona Kucybała, Andrzej Urbanik, Rafał Obuchowicz, Zbisław Tabor
CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer, European Conference on Artificial Intelligence [ECAI], (2023),
12.
Bartosz Zieliński, Dawid Rymarczyk, Joost van de Weijer , Bartłomiej Twardowski
ICICLE: Interpretable Class Incremental Continual Learning, IEEE International Conference on Computer Vision [ICCV](MAIN), (2023),
11.
Dawid Rymarczyk, Daniel Dobrowolski, Tomasz Danel
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction, SIAM International Conference on Data Mining [SDM], (2023),
10.
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 1481-1492
9.
Bartosz Zieliński, Adriana Borowa, Dawid Rymarczyk, Dorota Ochońska, Agnieszka Sroka-Oleksiak, Monika Brzychczy-Włoch
8.
Automating Patient-Level Lung Cancer Diagnosis in Different Data Regimes, International Conference on Neural Information Processing [ICONIP](MAIN) vol. Communications in Computer and Information Science 1794 (2022), 13–24
7.
Dawid Rymarczyk, Łukasz Struski, Michał Górszczak, Koryna Lewandowska, Jacek Tabor, Bartosz Zieliński
Interpretable Image Classification with Differentiable Prototypes Assignment, European Conference on Computer Vision [ECCV](MAIN) vol. Lecture Notes in Computer Science 13672 (2022), 351-368
6.
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database [ECML PKDD](MAIN) vol. Lecture Notes in Computer Science 13713 (2022), 421-436
5.
Adriana Borowa, Anna Bracha, Maurycy Chronowski, Wojciech Ozimek, Dawid Rymarczyk, Bartosz Zieliński
Comparison of Supervised and Self-supervised Deep Representations Trained on Histological Image, World Congress on Medical and Health Informatics [MEDINFO] vol. Studies in Health Technology and Informatics 290 (2021), 1052-1053
4.
ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification, ACM International Conference on Knowledge Discovery and Data Mining [KDD](MAIN), (2021), 1420-1430
3.
Deep learning classification of bacteria clones explained by persistence homology, IEEE International Joint Conference on Neural Networks [IJCNN], (2021), 1-8
2.
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning, IEEE Workshop on Applications of Computer Vision [WACV], (2021), 1720-1729
1.
Monika Brzychczy-Włoch, Adam Piekarczyk, Dawid Rymarczyk, Agnieszka Sroka-Oleksiak, Bartosz Zieliński
Granty (realizowane po maju 2009 roku)
Tytuł | Rola | Rozpoczęcie | Zakończenie |
---|---|---|---|
Poprawa własności interpretacji wyników głębokich sieci neuronowych w oparciu o części prototypiczne. | Kierownik | 2023-02-17 | 2024-02-16 |