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:

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 book series (2020), 12
21.
Mario A.T. Figueiredo, Mateusz Juda, Maciej Kołomycki, Łukasz Struski, Marek Śmieja
Iterative Imputation of Missing Data using Auto-encoder Dynamics, INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING [ICONIP] vol. Lecture Notes in Computer Science book series (2020), 12
20.
Learning from Incomplete Images using (Graph) Convolutional Neural Networks, INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING vol. Lecture Notes in Computer Science book series (LNCS, volume 12533) (2020), 12
19.
Spatial Graph Convolutional Networks, INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING vol. Communications in Computer and Information Science book series (CCIS, volume 1333) (2020), 8
18.
Mario A.T. Figueiredo, Mateusz Juda, Maciej Kołomycki, Łukasz Struski, Marek Śmieja
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
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
Teoria analizy niekompletnych danychWykonawca2016-07-132019-10-12