prof. dr hab. Jacek Tabor

Jednostki:

  • Wydział Matematyki i Informatyki UJ
  • Instytut Informatyki i Matematyki Komputerowej
  • Katedra Uczenia Maszynowego

HabilitacjaOtwarcie: 2006-04-27, Zamknięcie: 2008-06-05

ProfesuraOtwarcie: 2013-05-23, Zamknięcie: 2015-07-17

Publikacje:

149.
Przemysław Spurek, Marcin Przewięźlikowski, Jacek Tabor, Zięba Maciej , Przemysław Przybysz
148.
Przemysław Spurek, Wojciech Zając, Piotr Borycki, Joanna Waczyńska, Jacek Tabor, Zięba Maciej
NeRFlame: Flame-Based Conditioning of NeRF for 3D Face Rendering, International Conference on Computational Science [ICCS](MAIN) vol. 14832 (2024), 346--361
144.
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations, National Conference of the American Association for Artificial Intelligence [AAAI](MAIN) vol. 38 (19) (2024), 21563 - 21573
143.
Adrian Suwała, Bartosz Wójcik, Magdalena Proszewska, Jacek Tabor, Przemysław Spurek, Marek Śmieja
Face Identity-Aware Disentanglement in StyleGAN, IEEE Workshop on Applications of Computer Vision [WACV], (2024), 10
142.
Aleksandra Nowak, Bram Grooten, Decebal Constantin Mocanu , Jacek Tabor
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training, Advances in Neural Information Processing Systems [NeurIPS](MAIN), (2023),
141.
Bartosz Wójcik, Marcin Przewięźlikowski, Filip Szatkowski, Maciej Wołczyk, Klaudia Bałazy, Bartłomiej Krzepkowski, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński
140.
ProPML: Probability Partial Multi-label Learning, IEEE International Conference on Data Science and Advanced Analytics [DSAA], (2023), 1-8
139.
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] vol. 372 (2023), 2210 - 2217
138.
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
137.
Mohammadreza Banaei, Klaudia Bałazy, Artur Kasymov, Rémi Lebret, Jacek Tabor, Karl Aberer
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models, European Association of Computational Linguistics [EACL] vol. Findings of the Association for Computational Linguistics: EACL 2023 (2023), 1788–1805
136.
Przemysław Spurek, Karol Piczak, Jacek Tabor, Tomasz Trzciński, Filip Szatkowski
Hypernetworks build Implicit Neural Representations of Sounds, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database [ECML PKDD] vol. 14172 (2023), 661–676
135.
Przemysław Spurek, Marcin Sendera, Marcin Przewięźlikowski, Jan Miksa, Mateusz Rajski, Konrad Karanowski, Maciej Zieba, Jacek Tabor
134.
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
133.
Michał Znaleźniak, Przemysław Rola, Patryk Kaszuba, Jacek Tabor, Marek Śmieja
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
132.
Bounding Evidence and Estimating Log-Likelihood in VAE, International Conference on Artificial Intelligence and Statistics [AISTATS](MAIN) vol. 206 (2023), 5036-5051
130.
Przemysław Spurek, Jacek Tabor, Marcin Sendera, Marcin Przewięźlikowski, Konrad Karanowski, Zięba Maciej
Hypershot: Few-shot learning by kernel hypernetworks, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 2469--2478
129.
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts, IEEE Workshop on Applications of Computer Vision [WACV](MAIN) vol. 2023 (2023), 1481-1492
128.
SONGs: Self-Organizing Neural Graphs, IEEE Workshop on Applications of Computer Vision [WACV](MAIN), (2023), 3837-3846
125.
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
124.
Batch size reconstruction-distribution trade-off in kernel based generative autoencoders, IEEE International Conference on Image Processing [ICIP], (2022), 3728-3732
123.
Nonlinear Weighted Independent Component Analysis, International Conference on Information Processing and Management of Uncertainty [IPMU] vol. II (2022), 3–16
122.
Przemysław Spurek, Jacek Tabor, Piotr Tempczyk, Rafał Michaluk, Łukasz Garncarek, Adam Golinski
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood, International Conference on Machine Learning [ICML](MAIN), (2022), 21205-21231
121.
Jacek Tabor, Maciej Wołczyk, Karol Piczak, Bartosz Wójcik, Łukasz Pustelnik, Morawiecki Paweł, Tomasz Trzcinski, Przemysław Spurek
Continual Learning with Guarantees via Weight Interval Constraints, International Conference on Machine Learning [ICML](MAIN), (2022), 23897-23911
120.
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
118.
Weakly-supervised cell classification for effective High Content Screening, International Conference on Computational Science [ICCS](MAIN) vol. Lecture Notes in Computer Science 13350 (2022), 318–330
116.
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
115.
Łukasz Struski, Jacek Tabor, Szymon Bobek, Sławomir K. Tadeja, Przemysław Stachura, Timoleon Kipourus, Grzegorz Nalepa, Per Ola Kristensson
112.
Bartosz Wójcik, Morawiecki Paweł, Marek Śmieja, Tomasz Krzyżek, Przemysław Spurek, Jacek Tabor
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders, International Conference on Tools with Artificial Intelligence [ICTAI], (2021), 1322-1326
111.
Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Przemysław Spurek, Tomasz Trzciński, Zięba Maciej
Non-Gaussian Gaussian Processes for Few-Shot Regression, Advances in Neural Information Processing Systems [NeurIPS](MAIN) vol. 34 (2021), 10285-10298
110.
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks, Advances in Neural Information Processing Systems [NeurIPS](MAIN) vol. 34 (2021), 1-13
109.
Karl Aberer, Klaudia Bałazy, Mohammadreza Banaei, Rémi Lebret, Jacek Tabor
Direction is what you need: Improving Word Embedding Compression in Large Language Models, Proceedings of the 6th Workshop On Representation Learning for Nlp (repl4nlp-2021) (2021), 322–330
108.
Tomasz Danel, Łukasz Maziarka, Sabina Podlewska, Jacek Tabor, Agnieszka Wojtuch
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction, IEEE International Joint Conference on Neural Networks [IJCNN], (2021), 1-8
107.
ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification, ACM International Conference on Knowledge Discovery and Data Mining [KDD](MAIN) vol. 9781450383325 (2021), 1420-1430
106.
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning, IEEE Workshop on Applications of Computer Vision [WACV] vol. 978-1-6654-0477-8 (2021), 1720-1729
104.
Andrzej Bojarski, Stanisław Jastrzębski, Stefan Mordalski, Sabina Podlewska, Maciej Szymczak, Jacek Tabor, Agnieszka Wojtuch
103.
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models, European Symposium on Artificial Neural Networks [ESANN], (2020), 6
102.
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
101.
Non-linear ICA based on Cramer-Wold metric, International Conference On Neural Information Processing vol. Lecture Notes in Computer Science book series (LNCS, volume 12534) (2020),
100.
Zięba Maciej , Przemysław Spurek, Jacek Tabor, Tomasz Trzciński, Sebastian Winczowski, Maciej Zamorski
Hypernetwork approach to generating point clouds, International Conference on Machine Learning [ICML](MAIN), (2020),
99.
Finding the Optimal Network Depth in Classification Tasks, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008) [ECML PKDD], (2020),
98.
Devansh Arpit, Kyunghyun Cho, Stanislav Fort, Krzysztof J. Geras, Stanisław Jastrzębski, Maciej Szymczak, Jacek Tabor
The Break-Even Point on Optimization Trajectories of Deep Neural Networks, 8th International Conference On Learning Representations (2020),
97.
Andrzej Bojarski, Stanisław Jastrzębski, Stefan Mordalski, Sabina Podlewska, Maciej Szymczak, Jacek Tabor, Agnieszka Wojtuch
95.
Flow-based SVDD for anomaly detection, International Conference On Machine Learning (workshop Track) (2020),
93.
Stanisław Jastrzębski, Maciej A. Nowak, Jacek Tabor, Wojciech Tarnowski, Piotr Warchoł
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function, International Conference on Artificial Intelligence and Statistics [AISTATS], (2019), 10
92.
Andrzej Bojarski, Stanisław Jastrzębski, Damian Leśniak, Sabina Podlewska, Igor Sieradzki, Jacek Tabor
89.
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
88.
Tomasz Danel, Stanisław Jastrzębski, Łukasz Maziarka, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor
Molecule-Augmented Attention Transformer, Neural Information Processing Systems (workshop Track) (2019),
87.
Sylwester Klocek, Łukasz Maziarka, Jakub Nowak, Marek Śmieja, Jacek Tabor, Maciej Wołczyk
Hypernetwork Functional Image Representation, Artificial Neural Networks and Machine Learning – Icann 2019: Workshop and Special Sessions (2019), 496-510
82.
Andrzej Bojarski, Stanisław Jastrzębski, Damian Leśniak, Sabina Podlewska, Igor Sieradzki, Jacek Tabor
77.
Processing of missing data by neural networks, Advances in Neural Information Processing Systems vol. 31 (2018), 2719-2729
69.
R Package CEC , Neurocomputing vol. 237 (2017), 410–413
66.
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
65.
Maximum Likelihood Estimation and Optimal Coordinates, International Conference On Systems Science, 2016 vol. Springer (2016), 3-13
63.
Online Extreme Entropy Machines for Streams Classification and Active Learning, Advances in Intelligent Systems and Computing vol. 403 (2016), 371-381
60.
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
59.
Jacek Chudziak, Jacek Tabor, Józef Tabor
56.
Spherical Wards clustering and generalized Voronoi diagrams, Proceeding of Ieee International Conference On Data Science and Advanced Analytics vol. 36678 (2015), 10
49.
Alena Chaikouskaya, Przemysław Spurek, Jacek Tabor, Elżbieta Zając
A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves, Computer Information Systems and Industrial Management vol. 8838 (2014), 559-570
46.
Andrzej Bojarski, Marek Śmieja, Jacek Tabor, Dawid Warszycki
44.
Renyi entropy dimension of the mixture of measures, Proceedings of Science and Information Conference (2014), 685-689
42.
Detection of elliptical shapes via cross-entropy clustering, Pattern Recognition and Image Analysis Lecture Notes in Computer Science vol. 7887 (2013), 656-663
41.
Mahalanobis distance-based algorithm for ellipse growing in iris preprocessing, Computer Information Systems and Industrial Management Lecture Notes in Computer Science vol. 8104 (2013), 158-167
40.
Weighted Approach to Projective Clustering, Computer Information Systems and Industrial Management Lecture Notes in Computer Science vol. 8104 (2013), 367-378
39.
Jacek Tabor, Józef Tabor, Marek Żołdak
37.
Image segmentation with use of cross-entropy clustering, Advances in Intelligent Systems and Computing vol. 226 (2013), 403-409
36.
Przemysław Spurek, Jacek Tabor, Elżbieta Zając
Detection of Disk-Like Particles in Electron Microscopy Images, Advances in Intelligent Systems and Computing vol. 226 (2013), 411-417
32.
Appendix A: Semi-Hyperbolicity: Estimations vol. Random and Computational Dynamics 1 (2012), "Phil Diamond, Peter Kloeden, Victor Kozyakin, Alexei Pokrovskii, Semi-Hyperbolicity and Bi-Shadowing", American Institute of Mathematical Sciences
28.
A new algorithm for rotation detection in iris pattern recognition, Lecture Notes in Computer Science vol. Computer Information Systems and Industrial Management, Volume 7564/2012 (2012), 135-145
26.
Krzysztof Misztal, Emil Saeed, Khalid Saeed, Jacek Tabor
Iris Pattern Recognition with a New Mathematical Model to its Rotation Detection vol. 1 (2012), "Biometrics and Kansei Engineering", Springer Verlag (połaczony z Kluwer Academic Publishing)
25.
Approximately Midconvex Functions vol. Springer Optimization and Its Applications (2012), "Functional Equations in Mathematical Analysis", Springer Verlag (połaczony z Kluwer Academic Publishing)
22.
Anna Mureńko, Jacek Tabor, Józef Tabor
19.
18.
Adam Najdecki, Jacek Tabor, Józef Tabor
17.
15.
Jacek Mrowiec, Jacek Tabor, Józef Tabor
Approximately midconvex functions vol. International Series of Numerical Mathematics 157 (2009), "Inequalities and Applications", Birkhäuser
14.
Anna Mureńko, Jacek Tabor, Józef Tabor
12.
11.
1.
Semi-hyperbolicity implies hyperbolicity in the linear case, Universitatis Iagellonicae Acta Mathematica vol. 36 (1998), 121-126

Konferencje organizowane:

2.
Deep Learning Workshops, Kraków, 2018-02-20, 2018-02-23

Doktoranci (po 27 października 2003 roku)

DoktorantOtwarcieZakonczenie
Tomasz Kulaga2009-06-252012-10-25
Jakub Bielawski2010-06-242013-04-25
Krzysztof Misztal2011-05-262015-04-30
Przemysław Spurek2012-05-312014-06-26
Łukasz Struski2012-05-312014-01-30
Łukasz Struski2013-03-282014-01-30
Marek Śmieja2013-06-272015-01-29
Wojciech Czarnecki2014-06-262015-12-17
Magdalena Wiercioch2016-06-30 
Stanisław Jastrzębski2017-06-292019-03-28
Andrzej Bedychaj2020-09-24 

Recenzje (po 27 października 2003 roku)

RecenzowanyJednostkaTreść recenzji
Doktorat: Natalia ŻelaznaKatedra Matematyki Obliczeniowej 
Doktorat: Krzysztof WesołowskiKatedra Teorii Aproksymacji 

Granty (realizowane po maju 2009 roku)

TytułRolaRozpoczęcieZakończenie
Uogólnienie entropii i wymiaru entropijnego oraz ich zastosowaniaKierownik2011-12-082014-12-07
Paradygmat minimalizacji pamięci w klastrowaniuKierownik2015-01-212017-08-26
Teoria analizy niekompletnych danychKierownik2016-07-132019-10-12
Efektywne metody uczenia nienadzorowanego z zastosowaniami w głębokim nauczaniuKierownik2018-02-092021-02-08
Sztuczne sieci neuronowe inspirowane biologicznieKierownik2019-09-012023-11-29
Głębokie samoorganizujące się grafy neuronoweKierownik2022-02-042025-02-03
Meta-uczenie w głębokich sieciach neuronowychKierownik2024-01-292028-01-28