Achievements

Our prizes, grants and other accomplishments.

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Research

Classification methods

We are investigating new models able to perform classification. Including hierarchical, support vector based and entropy based models.

Clustering methods

Our group develops new clustering algorithms, based mostly on information and compression theory.

Deep learning

GMUM is also actively working on deep learning paradigm (in the Hinton's sense), as we strongly believe that this is the future of machine learning.

Recent papers

Przemysław Spurek, Łukasz Struski, Jacek Tabor Lossy Compression Approach to Subspace Clustering, INFORM SCIENCES vol. 435 (2018), 161-183
Krzysztof Byrski, Przemysław Spurek, Jacek Tabor Online updating of active function cross-entropy clustering, PATTERN ANAL APPL (2018),
Igor Podolak, Adam Roman, Marek Szykuła, Bartosz Zieliński A machine learning approach to synchronization of automata, EXPERT SYST APPL (2018), 357-371
Mateusz Juda, Markus Seidl, Matthias Zeppelzauer, Bartosz Zieliński A Study on Topological Descriptors for the Analysis of 3D Surface Texture, COMPUT VIS IMAGE UND vol. 167 (2018), 74-88
K Rataj, W Czarnecki, S Podlewska, A Pocha, AJ Bojarski, Substructural Connectivity Fingerprint and Extreme Entropy Machines—A New Method of Compound Representation and Analysis, Molecules 23 (6), 1242 (2018)
WM Czarnecki, SM Jayakumar, M Jaderberg, L Hasenclever, YW Teh, ..., Mix&Match-Agent Curricula for Reinforcement Learning, ICML 2018 (2018)
S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ..., Kickstarting Deep Reinforcement Learning, NIPS 2018 DL Workshop (2018)
J Schwarz, J Luketina, WM Czarnecki, A Grabska-Barwinska, YW Teh, ..., Progress & Compress: A scalable framework for continual learning, ICML 2018 (2018)
JX Wang, E Hughes, C Fernando, WM Czarnecki, EA Duenez-Guzman, ..., Evolving intrinsic motivations for altruistic behavior, arXiv preprint arXiv:1811.05931 (2018)
M Hessel, H Soyer, L Espeholt, W Czarnecki, S Schmitt, H van Hasselt, Multi-task deep reinforcement learning with popart, AAAI 2019 (2018)
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Achievements

Grants

Efficient unsupervised learning with applications in deep learning

prof. Jacek Tabor, dr Marek Śmieja, dr Przemysław Spurek, dr Łukasz Struski
2018-2021, NCN OPUS 13

Additional information in data clustering and related areas

dr Marek Śmieja, dr Przemysław Spurek, dr Łukasz Struski
2017-2020 NCN SONATA 11

Theory of missing data

prof. Jacek Tabor, dr Łukasz Struski, dr Marek Śmieja, dr Bartosz Zieliński
2016-2019, NCN OPUS 10

Clustering algorithm, which uses generalized Gaussian distribution and non-normal distributions

dr Przemysław Spurek
2016-2019, NCN SONATA 10

Detectors and descriptors of the key points based on the topological information

dr Bartosz Zieliński, dr Mateusz Juda
2016-2019, NCN SONATA 10

Algorithmical aspects of synchronization

dr Adam Roman
2016-2019, NCN OPUS 9

Prizes

Deep Learning in representation of long sequential data

mgr Konrad Żołna
2018-2019, NCN ETIUDA 6

New representation learning methods in Deep Learning

mgr Stanisław Jastrzębski
2017-2019, NCN ETIUDA 5

Rector prize for scientific achievements

prof. Jacek Tabor, dr Wojciech Czarnecki, dr Przemysław Spurek
2015, UJ

Scholarship of Polish Minister Of Science

lic. Stanisław Jastrzębski
2015, MNiSW

Best paper award at CORES'15

mgr Wojciech Czarnecki and dr hab. Jacek Tabor
2015

4th place in TRADESHIFT competition

lic. Stanisław Jastrzębski, Rafał Józefowicz (Google), mgr Wojciech Czarnecki
2014

Contact Us

Igor Podolak, PhD
Group of Machine Learning Research
Faculty of Mathematics and Computer Science
ul. Lojasiewicza 6
30-342, Cracow, Poland