dr hab. Marek Śmieja
Jednostki:
- Wydział Matematyki i Informatyki UJ
- Instytut Informatyki i Matematyki Komputerowej
- Katedra Uczenia Maszynowego
Doktorat Otwarcie: 2013-06-27, Zamknięcie: 2015-01-29
Habilitacja Otwarcie: 2020-11-25, Zamknięcie: 2022-12-15
Publikacje:
64.
Bartosz Zieliński, Marcin Przewięźlikowski, Mateusz Pyla, Bartłomiej Twardowski, Jacek Tabor, Marek Śmieja
63.
Zięba Maciej , Marcin Przewięźlikowski, Marek Śmieja, Jacek Tabor, Tomasz Trzciński, Przemysław Spurek
RegFlow: Probabilistic Flow-Based Regression for Future Prediction, Asian Conference on Intelligent Information and Database Systems [ACIIDS], (2024), 267–279
62.
Andrzej Bedychaj, Jacek Tabor, Marek Śmieja
StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN, Pacific-Asia Conference on Knowledge Discovery and Data Mining [PAKDD], (2024), 118-130
61.
Bartosz Zieliński, Marcin Przewięźlikowski, Marcin Osial, Marek Śmieja
A deep cut into Split Federated Self-Supervised Learning, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database [ECML PKDD](MAIN), (2024), 444-459
60.
Ewelina Jamrozik, Sabina Podlewska, Marek Śmieja
59.
Magdalena Proszewska, Maciej Wołczyk, Zięba Maciej , Patryk Wielopolski, Łukasz Maziarka, Marek Śmieja
58.
Face Identity-Aware Disentanglement in StyleGAN, IEEE Workshop on Applications of Computer Vision [WACV], (2024), 10
57.
Witold Wydmański, Oleksii Bulenok, Marek Śmieja
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets, IEEE International Conference on Data Science and Advanced Analytics [DSAA], (2023), 9
56.
Bartosz Wójcik, Marcin Przewięźlikowski, Filip Szatkowski, Maciej Wołczyk, Klaudia Bałazy, Bartłomiej Krzepkowski, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński
Zero time waste in pre-trained early exit neural networks, Neural Networks vol. 168 (2023), 580-601
55.
r-softmax: Generalized Softmax with Controllable Sparsity Rate, International Conference on Computational Science [ICCS] vol. Lecture Notes in Computer Science, vol 14074. Springer, Cham (2023), 137-145
54.
ChiENN: Embracing Molecular Chirality with Graph Neural Networks, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database [ECML PKDD] vol. Lecture Notes in Computer Science(), vol 14171. Springer, Cham (2023), 36-52
53.
Contrastive Hierarchical Clustering, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database [ECML PKDD] vol. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. (2023), 627–643
52.
Morawiecki Paweł, Andrii Krutsylo, Maciej Wołczyk, Marek Śmieja
Hebbian Continual Representation Learning, Hawaii International Conference on System Sciences [HICSS], (2023), 1259-1268
51.
SONGs: Self-Organizing Neural Graphs, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 3837-3846
50.
SLOVA: Uncertainty estimation using single label one-vs-all classifier, Applied Soft Computing Journal vol. 126 (2022), 109219
49.
48.
Bernhard C. Geiger, Sophie Steger, Marek Śmieja
Semi-Supervised Clustering via Information-Theoretic Markov Chain Aggregation, ACM Symposium on Applied Computing [SAC](MAIN), (2022), 1136-1139
47.
Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Zięba Maciej , Patryk Wielopolski, Rafał Kurczab, Marek Śmieja
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models, National Conference of the American Association for Artificial Intelligence [AAAI](MAIN) vol. 36/8 (2022), 8647-8656
46.
MisConv: Convolutional Neural Networks for Missing Data, IEEE Workshop on Applications of Computer Vision [WACV], (2022), 2060-2069
45.
44.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders, International Conference on Tools with Artificial Intelligence [ICTAI], (2021), 1322-1326
43.
Klaudia Bałazy, Igor Podolak, Marek Śmieja, Jacek Tabor, Tomasz Trzciński, Maciej Wołczyk, Bartosz Wójcik
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks, Advances in Neural Information Processing Systems [NeurIPS](MAIN) vol. 34 (2021), 1-13
42.
Dawid Warszycki, Łukasz Struski, Marek Śmieja, Rafał Kafel, Rafał Kurczab
41.
SeGMA: Semi-Supervised Gaussian Mixture Autoencoder, IEEE Transactions on Neural Networks and Learning Systems vol. 32/9 (2021), 3930-3941
40.
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models, European Symposium on Artificial Neural Networks [ESANN], (2020), 6
39.
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
38.
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
37.
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
36.
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
35.
Can auto-encoders help with filling missing data?, International Conference On Learning Represenation (workshop Track) (2020), 6
34.
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
33.
Processing of incomplete images by (graph) convolutional neural networks, International Conference On Machine Learning (workshop Track) (2020),
32.
Flow-based SVDD for anomaly detection, International Conference On Machine Learning (workshop Track) (2020),
31.
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
30.
Pointed subspace approach to incomplete data, Journal of Classification vol. 37 (2020), 42-57
29.
Generalized RBF kernel for incomplete data, Knowledge-Based Systems vol. 173 (2019), 150-162
28.
27.
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
26.
Hypernetwork Functional Image Representation, Artificial Neural Networks and Machine Learning – Icann 2019: Workshop and Special Sessions (2019), 496-510
25.
24.
Projected memory clustering, Pattern Recognition Letters vol. 123 (2019), 9-15
23.
SVM with a neutral class, Pattern Analysis and Applications vol. 22/2 (2019), 573-582
22.
21.
Processing of missing data by neural networks, Advances in Neural Information Processing Systems vol. 31 (2018), 2719-2729
20.
Oleksandr Myronov, Marek Śmieja, Jacek Tabor
Semi-supervised discriminative clustering with graph regularization, Knowledge-Based Systems vol. 151 (2018), 24-36
19.
Bernhard C. Geiger, Marek Śmieja
Semi-supervised cross-entropy clustering with information bottleneck constraint, Information Sciences vol. 421 (2017), 245-271
18.
Regression SVM for incomplete data, Schedae Informaticae vol. 26 (2017), 23-35
17.
Rafał Kafel, Marek Śmieja, Dawid Warszycki
16.
15.
R Package CEC , Neurocomputing vol. 237 (2017), 410–413
14.
13.
Szymon Nakoneczny, Marek Śmieja
Natural language processing methods in biological activity prediction, Proceedings of Ecml Pkdd Workshop On Machine Learning in Life Sciences (2016), 25-36
12.
Szymon Nakoneczny, Marek Śmieja, Jacek Tabor
Fast entropy clustering of sparse high dimensional binary data, Proceednigs of Ieee International Joint Conference On Neural Networks (ijcnn 2016) (2016), 2397-2404
11.
Marek Śmieja, Dawid Warszycki
Average Information Content Maximization - a new approach for fingerprint hybridization and reduction, PLoS One vol. 11/1 (2016), e0146666
10.
Probability Index of Metric Correspondence as a measure of visualization reliability, Proceedings of Ecml Pkdd Workshop On Machine Learning in Life Sciences (2015), 16-27
9.
Spherical Wards clustering and generalized Voronoi diagrams, Proceeding of Ieee International Conference On Data Science and Advanced Analytics vol. 36678 (2015), 10
8.
Entropy approximation in lossy source coding problem, Entropy vol. 17/5 (2015), 3400-3418
7.
Mixture of metrics optimization for machine learning problems, Schedae Informaticae vol. 24 (2015), 133-142
6.
Weighted approach to general entropy function, IMA Journal of Mathematical Control and Information vol. 32/2 (2015), 329-327
5.
4.
Andrzej Bojarski, Marek Śmieja, Jacek Tabor, Dawid Warszycki
Asymmetric Clustering Index in a case study of 5-HT1A receptor ligands, PLoS One vol. 9(7) (2014), e102069
3.
Renyi entropy dimension of the mixture of measures, Proceedings of Science and Information Conference (2014), 685-689
2.
Image segmentation with use of cross-entropy clustering, Advances in Intelligent Systems and Computing vol. 226 (2013), 403-409
1.
Entropy of the mixture of source and entropy dimension, IEEE Transactions on Information Theory vol. 58(5) (2012), 2719-2728
Konferencje:
27.
ICLR International Conference on Learning Representatons, ICLR, Addis Ababa, Etiopia (virtual conference), 2020-04-26 - 2020-05-01
26.
TFML 3rd International Conference on Theoretical Foundations of Machine Learning, Uniwersytet Jagielloński, Kraków, Polska, 2019-02-11 - 2019-02-15
25.
Machine Learning Nokia Workshop, Nokia, Kraków, Polska, 2019-01-17 - 2019-01-17
24.
PL in ML: Polish View on Machine Learning, Uniwersytet Warszawski, Warszawa, Polska, 2018-12-14 - 2018-12-17
23.
NIPS 32nd International Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, Montreal, Kanada, 2018-12-02 - 2018-12-08
22.
ICML 35th International Conference on Machine Learning, International Machine Learning Society, Sztokholm, Szwecja, 2018-07-10 - 2018-07-15
21.
International Conference on Statistical Challenges in 21st Century Cosmology, Universitat de Valencia, Walencja, Hiszpania, 2018-05-22 - 2018-05-25
20.
eKNOW 10th International Conference on Information, Process, and Knowledge Management, International Academy, Research, and Industry Association, Rzym, Włochy, 2018-03-25 - 2018-03-29
19.
IDA 16th International Symposium on Intelligent Data Analysis, Birkbeck, University of London, UK, Londyn, Wielka Brytania, 2017-10-26 - 2017-10-28
18.
ICML 34th International Conference on Machine Learning, International Machine Learning Society, Sydney, Australia, 2017-08-06 - 2017-08-11
17.
TFML 2nd International Conference on Theoretical Foundations of Machine Learning, Uniwersytet Jagielloński, Kraków, Polska, 2017-02-13 - 2017-02-16
16.
ECML PKDD Workshop on Machine Learning in Life Science, University of Trento, Rive del Garda, Włochy, 2016-09-19 - 2016-09-23
15.
IJCNN IEEE International Joint Conference on Neural Networks, IEEE Computational Intelligence Society, Vancouver, Kanada, 2016-07-16 - 2016-07-29
14.
DSAA IEEE International Conference on Data Science and Advanced Analytics, IEEE Computational Intelligence Society, Paryż, Francja, 2015-10-19 - 2015-10-21
13.
MVML International Conference on Machine Vision and Machine Learning, International ASET Inc., Barcelona, Hiszpania, 2015-07-13 - 2015-07-14
12.
CISIM 13th International Conference on Computer Information Systems and Industrial Management Applications, Ton Duc Thang University, Ho Chi Minh City, Wietnam, 2014-11-05 - 2014-11-07
11.
XLIII Conference on Applied Mathematics, Instytut Matematyczny PAN, Zakopane, Polska, 2014-09-02 - 2014-09-09
10.
SAI Science and Information Conference, Science and Information Conferences, Londyn, Wielka Brytania, 2014-08-27 - 2014-08-29
9.
8th Annual International Conference on Mathematics and Statistics, The Athens Institute for Education and Research, Ateny, Grecja, 2014-06-30 - 2014-06-03
8.
AISTATS 17th International Conference on Artificial Intelligence and Statistics, Artificial Intelligence and Statistics Society, Rejkiawik, Islandia, 2014-04-22 - 2014-04-25
7.
CISIM 12th International Conference on Computer Information Systems and Industrial Management Applications, AGH, Kraków, Polska, 2013-09-25 - 2013-09-27
6.
XLII Conference of Applied Mathematics, Instytut Matematyczny PAN, Zakopane, Polska, 2013-08-27 - 2013-09-03
5.
Between Theory and Applications, Uniwersytet Gdański, Będlewo, Polska, 2013-06-16 - 2013-06-22
4.
CORES 8th International Conference on Computer Recognition Systems, Politechnika Wrocławska, Miłków, Polska, 2013-05-27 - 2013-05-29
3.
ITW Information Theory Workshop, IEEE Information Theory Society, Lozanna, Szwajcaria, 2012-09-03 - 2012-09-07
2.
MaxEnt 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxPlanck Institute, Garching, Niemcy, 2012-07-15 - 2012-07-20
1.
XL Conference of Applied Mathematics, Instytut Matematyczny PAN, Zakopane, Polska, 2011-08-30 - 2011-09-06
Konferencje organizowane:
2.
Theoretical Foundations of Machine Learning, Będlewo, 2015-02-16, 2015-02-20
1.
Theoretical Foundations of Machine Learning - 1TFML 2017, Kraków, 2017-02-13, 2017-02-17
Granty (realizowane po maju 2009 roku)
| Tytuł | Rola | Rozpoczęcie | Zakończenie |
|---|---|---|---|
| Uogólnienie entropii i wymiaru entropijnego oraz ich zastosowania | Wykonawca | 2011-12-08 | 2014-12-07 |
| Klastrowanie w przestrzeniach metrycznych | Kierownik | 2013-07-12 | 2015-07-11 |
| Rozwój metod uczenia maszynowego z zastosowaniem do przewidywania aktywności związków chemicznych | Kierownik | 2015-02-25 | 2016-12-24 |
| Paradygmat minimalizacji pamięci w klastrowaniu | Wykonawca | 2015-01-21 | 2017-08-26 |
| Teoria analizy niekompletnych danych | Wykonawca | 2016-07-13 | 2019-10-12 |
| Dodatkowa informacja w grupowaniu danych i zagadnieniach pokrewnych | Kierownik | 2017-01-26 | 2020-01-25 |
| Głębokie przetwarzanie danych strukturalnych | Kierownik | 2019-07-26 | 2023-07-25 |
| Głębokie warunkowe modele generatywne | Kierownik | 2023-01-11 | 2026-01-10 |
Nagrody
| Rok | Rodzaj | Rodzaj uhonorowanej działalności | Typ | |
|---|---|---|---|---|
| 2021 | nagroda | wybitny dorobek naukowy lub artystyczny | krajowa | Szczegóły |