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:
22.
Marcin Przewięźlikowski, Łukasz Struski, Marek Śmieja
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, Łukasz Maziarka, Agnieszka Słowik, Przemysław Spurek, Łukasz Struski, Marek Śmieja, Jacek Tabor
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, Łukasz Struski, Marek Śmieja
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.
Mario A.T. Figueiredo, Łukasz Struski, Marek Śmieja
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, J CLASSIF vol. 37 (2020), 42-57
12.
Generalized RBF kernel for incomplete data, KNOWL-BASED SYST 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 RECOGN LETT 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, INFORM SCIENCES vol. 435 (2018), 161-183
6.
Expansivity implies existence of Holder continuous Lyapunov function, DISCRETE CONT DYN-B vol. 22 (9) (2017), 3575-3589
5.
Regression SVM for incomplete data, SCHEDAE INFORMATICAE vol. 26 (2017), 23-35
4.
Semi-supervised model-based clustering with controlled clusters leakage, EXPERT SYST APPL vol. 85 (2016), 146-157
3.
Subspace memory clustering, SCHEDAE INFORMATICAE vol. 24 (2015), 133-142
2.
Expansivity and Cone-fields in Metric Spaces, J DYN DIFFER EQU vol. 26(3) (2014), 517-527
1.
Cone-Fields without Constant Orbit Core Dimension, DISCRETE CONT DYN-A 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 |
|---|---|---|---|
| Teoria analizy niekompletnych danych | Wykonawca | 2016-07-13 | 2019-10-12 |