dr Łukasz Struski
Jednostki:
- Wydział Matematyki i Informatyki UJ
- Instytut Matematyki
- Katedra Matematyki Stosowanej
Doktorat Otwarcie: 2012-05-31, Zamknięcie: 2014-01-30
Doktorat Otwarcie: 2013-03-28, Zamknięcie: 2014-01-30
Publikacje:
36.
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023),
35.
SONGs: Self-Organizing Neural Graphs, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 3848--3857
34.
Romuald A. Janik, Igor Podolak, Łukasz Struski, Anna Ceglarek, Koryna Lewandowska, Barbara Sikora-Wachowicz, Tadeusz Marek, Magdalena Fąfrowicz
33.
32.
Przemysław Spurek, Artur Kasymov, Marcin Mazur, Diana Janik, Sławomir K. Tadeja, Łukasz Struski, Jacek Tabor, Tomasz Trzciński
HyperPocket: generative point cloud completion, IEEE/RSJ International Conference on Intelligent Robots and Systems [IROS], (2022), 6848-6853
31.
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
30.
LocoGAN—Locally convolutional GAN, Computer Vision and Image Understanding vol. 221 (2022), 103462
29.
Romuald A. Janik, Igor T. Podolak, Łukasz Struski, Anna Ceglarek, Koryna Lewandowska, Barbara Sikora-Wachowicz, Tadeusz Marek, Magdalena Fafrowicz
28.
MisConv: Convolutional Neural Networks for Missing Data, IEEE Workshop on Applications of Computer Vision [WACV], (2022), 2060-2069
27.
Łukasz Struski, Jacek Tabor, Szymon Bobek, Sławomir K. Tadeja, Przemysław Stachura, Timoleon Kipourus, Grzegorz Nalepa, Per Ola Kristensson
26.
Missing Glow Phenomenon: learning disentangled representation of missing data, International Conference on Neural Information Processing [ICONIP] vol. vol 1516 (2021), 196-204
25.
Dawid Warszycki, Łukasz Struski, Marek Śmieja, Rafał Kafel, Rafał Kurczab
24.
23.
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
22.
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
Estimating conditional density of missing values using deep Gaussian mixture model, International Conference on Neural Information Processing [ICONIP] vol. Lecture Notes in Computer Science volume 12534 (2020), 220-231
21.
Iterative Imputation of Missing Data using Auto-encoder Dynamics, International Conference on Neural Information Processing [ICONIP] vol. Lecture Notes in Computer Science, volume 12534 (2020), 258-269
20.
Processing of Incomplete Images by (Graph) Convolutional Neural Networks, International Conference On Neural Information Processing vol. Lecture Notes in Computer Science, volume 12533 (2020), 512-523
19.
Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka
Spatial Graph Convolutional Networks, International Conference On Neural Information Processing vol. Communications in Computer and Information Science book series (CCIS, volume 1333) (2020), 668-675
18.
Can auto-encoders help with filling missing data?, International Conference On Learning Represenation (workshop Track) (2020), 6
17.
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
Estimating conditional density of missing values using deep Gaussian mixture model, International Conference On Machine Learning (workshop Track) (2020), 6
16.
Processing of incomplete images by (graph) convolutional neural networks, International Conference On Machine Learning (workshop Track) (2020),
15.
Flow-based SVDD for anomaly detection, International Conference On Machine Learning (workshop Track) (2020),
14.
Marek Śmieja, Łukasz Struski, Mario A.T. Figueiredo
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints, Neural Networks vol. 127 (2020), 193-203
13.
Pointed subspace approach to incomplete data, Journal of Classification vol. 37 (2020), 42-57
12.
Generalized RBF kernel for incomplete data, Knowledge-Based Systems vol. 173 (2019), 150-162
11.
Set Aggregation Network as a Trainable Pooling Layer, International Conference On Neural Information Processing vol. Lecture Notes in Computer Science book series (LNCS, volume 11954) (2019), 419-431
10.
Projected memory clustering, Pattern Recognition Letters vol. 123 (2019), 9-15
9.
8.
Processing of missing data by neural networks, Advances in Neural Information Processing Systems vol. 31 (2018), 2719-2729
7.
Lossy Compression Approach to Subspace Clustering, Information Sciences vol. 435 (2018), 161-183
6.
5.
Regression SVM for incomplete data, Schedae Informaticae vol. 26 (2017), 23-35
4.
3.
Subspace memory clustering, Schedae Informaticae vol. 24 (2015), 133-142
2.
Expansivity and Cone-fields in Metric Spaces, Journal of Dynamics and Differential Equations vol. 26(3) (2014), 517-527
1.
Cone-Fields without Constant Orbit Core Dimension, Discrete and Continuous Dynamical Systems vol. 32(10) (2012), 3651-3664
Konferencje:
10.
TFML 3rd International Conference on Theoretical Foundations of Machine Learning, Uniwersytet Jagielloński, Kraków, Polska, 2019-02-11 - 2019-02-15
9.
Machine Learning Nokia Workshop, Nokia, Kraków, Polska, 2019-01-17 - 2019-01-17
8.
PL in ML: Polish View on Machine Learning, Uniwersytet Warszawski, Warszawa, Polska, 2018-12-14 - 2018-12-17
7.
NIPS 32nd International Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, Montreal, Kanada, 2018-12-02 - 2018-12-08
6.
ICML 35th International Conference on Machine Learning, International Machine Learning Society, Sztokholm, Szwecja, 2018-07-10 - 2018-07-15
5.
AI @ Samsung, Samsung, Kraków, Polska, 2018-05-25 - 2018-05-25
4.
ICML 34th International Conference on Machine Learning, International Machine Learning Society, Sydney, Australia, 2017-08-06 - 2017-08-11
3.
Polish-SIGML 2017 - Polska Grupa Badawcza Systemów Uczących się , Department of Machine Learning, Institute of Computer Science and Computational Mathematics, Faculty, Kraków, Poland, 2017-02-17 - 2017-02-17
2.
Theoretical Foundations of Machine Learning 2017, Department of Machine Learning, Institute of Computer Science and Computational Mathematics, Faculty, Kraków, Poland, 2017-02-13 - 2017-02-17
1.
Theoretical Foundations of Machine Learning, Department of Machine Learning, Institute of Computer Science and Computational Mathematics, Faculty, Będlewo, Poland, 2015-02-16 - 2015-02-21
Konferencje organizowane:
2.
Theoretical Foundations of Machine Learning 2017, Kraków, 2017-02-13, 2017-02-17
1.
Polish-SIGML 2017 - Polska Grupa Badawcza Systemów Uczących się, Kraków, 2017-02-17, 2017-07-07
Granty (realizowane po maju 2009 roku)
| Tytuł | Rola | Rozpoczęcie | Zakończenie |
|---|---|---|---|
| Rzadkie i dyskretne reprezentacje w ukrytych przestrzeniach | Kierownik | 2021-07-15 | 2024-07-14 |
| Teoria analizy niekompletnych danych | Wykonawca | 2016-07-13 | 2019-10-12 |