dr Łukasz Struski

Jednostki:

  • Wydział Matematyki i Informatyki UJ
  • Instytut Matematyki
  • Katedra Matematyki Stosowanej

DoktoratOtwarcie: 2012-05-31, Zamknięcie: 2014-01-30

DoktoratOtwarcie: 2013-03-28, Zamknięcie: 2014-01-30

Publikacje:

42.
ProPaLL: Probabilistic Partial Label Learning, International Conference on Neural Information Processing [ICONIP](MAIN), (2023),
41.
ProPML: Probability Partial Multi-label Learning, IEEE International Conference on Data Science and Advanced Analytics [DSAA], (2023),
40.
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),
39.
38.
Bounding Evidence and Estimating Log-Likelihood in VAE, International Conference on Artificial Intelligence and Statistics [AISTATS](MAIN) vol. 206 (2023), 5036-5051
36.
35.
SONGs: Self-Organizing Neural Graphs, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 3837-3846
34.
Romuald A. Janik, Igor Podolak, Łukasz Struski, Anna Ceglarek, Koryna Lewandowska, Barbara Sikora-Wachowicz, Tadeusz Marek, Magdalena Fąfrowicz
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
29.
Romuald A. Janik, Igor T. Podolak, Łukasz Struski, Anna Ceglarek, Koryna Lewandowska, Barbara Sikora-Wachowicz, Tadeusz Marek, Magdalena Fafrowicz
28.
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski, Jacek Tabor
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
25.
Missing Glow Phenomenon: learning disentangled representation of missing data, International Conference on Neural Information Processing [ICONIP] vol. vol 1516 (2021), 196-204
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.
Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mario A.T. Figueiredo
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.
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.
Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mario A.T. Figueiredo
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
15.
Flow-based SVDD for anomaly detection, International Conference On Machine Learning (workshop Track) (2020),
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
8.
Processing of missing data by neural networks, Advances in Neural Information Processing Systems vol. 31 (2018), 2719-2729

Konferencje:

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:

Granty (realizowane po maju 2009 roku)

TytułRolaRozpoczęcieZakończenie
Rzadkie i dyskretne reprezentacje w ukrytych przestrzeniachKierownik2021-07-152024-07-14
Teoria analizy niekompletnych danychWykonawca2016-07-132019-10-12