prof. dr hab. Jacek Tabor dr hab. Igor T. Podolak dr Jarek Duda dr Krzysztof Misztal dr hab. Adam Roman dr Marek Śmieja dr Przemysław Spurek dr Marcin Żelawski dr Bartosz Zieliński dr Łukasz Struski dr Wojciech M. Czarnecki
mgr Stanisław Jastrzębski mgr Damian Leśniak mgr Maciej J. Mikulski mgr Agnieszka Pocha mgr Igor Sieradzki mgr Magdalena Wiercioch mgr Konrad Żołna mgr Jakub Chłędowski mgr Piotr Kowenzowski mgr Łukasz Maziarka

2018

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)
J Schwarz, J Luketina, WM Czarnecki, A Grabska-Barwinska, YW Teh, ..., Progress & Compress: A scalable framework for continual learning, ICML 2018 (2018)
WM Czarnecki, SM Jayakumar, M Jaderberg, L Hasenclever, YW Teh, ..., Mix&Match-Agent Curricula for Reinforcement Learning, ICML 2018 (2018)
M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ..., Human-level performance in first-person multiplayer games with population-based deep reinforcement learning, arXiv preprint arXiv:1807.01281 (2018)
M Hessel, H Soyer, L Espeholt, W Czarnecki, S Schmitt, H van Hasselt, Multi-task deep reinforcement learning with popart, arXiv preprint arXiv:1809.04474 (2018)
Przemysław Spurek, Łukasz Struski, Marek Śmieja, Jacek Tabor, Bartosz Zieliński Processing of missing data by neural networks, ADVANCES IN NEURAL INFORMATION PROCESSING (NIPS) (2018),
Oleksandr Myronov, Marek Śmieja, Jacek Tabor Semi-supervised discriminative clustering with graph regularization, KNOWL-BASED SYST vol. 151 (2018), 24-36
S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ..., Kickstarting Deep Reinforcement Learning, arXiv preprint arXiv:1803.03835 (2018)

2017

Andrzej Bojarski, Wojciech Czarnecki, Stanisław Jastrzębski, Damian Leśniak, Sabina Podlewska Quo vadis G Protein-Coupled Receptor ligands? A tool for analysis of the emergence of new groups of compounds over time, BIOORG MED CHEM LETT vol. 27 (2017), 626-631
Bernhard C. Geiger, Marek Śmieja Semi-supervised cross-entropy clustering with information bottleneck constraint, INFORM SCIENCES vol. 421 (2017), 245-271
Przemysław Spurek, Marek Śmieja, Jacek Tabor SVM with a neutral class, PATTERN ANAL APPL (2017), 17
WM Czarnecki, S Osindero, M Jaderberg, G Świrszcz, R Pascanu, Sobolev Training for Neural Networks, NIPS 2017 (2017)
WM Czarnecki, G Świrszcz, M Jaderberg, S Osindero, O Vinyals, ..., Understanding Synthetic Gradients and Decoupled Neural Interfaces, ICML 2017 (2017)
YW Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, ..., Distral: Robust Multitask Reinforcement Learning, NIPS 2017 (2017)
M Jaderberg, WM Czarnecki, S Osindero, O Vinyals, A Graves, ..., Decoupled neural interfaces using synthetic gradients, ICML 2017 (2017)
M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ..., Reinforcement learning with unsupervised auxiliary tasks, ICLR 2017 (2017)
P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ..., Value-decomposition networks for cooperative multi-agent learning, AAMAS 2018 (2017)
S Podlewska, WM Czarnecki, R Kafel, AJ Bojarski, Creating the new from the old: Combinatorial libraries generation with machine-learning-based compound structure optimization, Journal of chemical information and modeling 57 (2), 133-147 (2017)
Wojciech Czarnecki, Stanisław Jastrzębski, Damian Leśniak How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks, ARXIV (2017),
K Janocha, WM Czarnecki, On loss functions for deep neural networks in classification, TFML 2017 (2017)
M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ..., Population based training of neural networks, arXiv preprint arXiv:1711.09846 (2017)
Marek Śmieja, Magdalena Wiercioch Constrained clustering with a complex cluster structure, ADV DATA ANAL CLASSI vol. 11/3 (2017), 493-518
Rafał Kafel, Marek Śmieja, Dawid Warszycki Practical application of the Average Information Content Maximization (AIC-MAX) algorithm – selection of the most important structural features for serotonin receptor ligands, MOL DIVERS vol. 21/2 (2017), 407-412
Konrad Kamieniecki, Krzysztof Misztal, Przemysław Spurek, Marek Śmieja, Jacek Tabor R Package CEC , NEUROCOMPUTING vol. 237 (2017), 410–413
Łukasz Struski, Marek Śmieja, Jacek Tabor, Bartosz Zieliński Regression SVM for incomplete data, SCHEDAE INFORMATICAE (2017),

2016

Łukasz Struski, Marek Śmieja, Jacek Tabor Semi-supervised model-based clustering with controlled clusters leakage, EXPERT SYST APPL vol. 85 (2016), 146-157
Marek Śmieja, Dawid Warszycki Average Information Content Maximization - a new approach for fingerprint hybridization and reduction, PLOS ONE vol. 11/1 (2016), e0146666
Wojciech Czarnecki, Stanisław Jastrzębski, Damian Leśniak Learning to SMILE(S), INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (WORKSHOP TRACK) (2016),
G Swirszcz, WM Czarnecki, R Pascanu, Local minima in training of neural networks, arXiv preprint arXiv:1611.06310 (2016)
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
Phong B. Dao, Tomasz Barszcz, Wiesław J. Staszewski, Konrad Żołna Towards homoscedastic nonlinear cointegration for structural health monitoring, MECH SYST SIGNAL PR vol. 2016 (2016), 15
Wojciech Czarnecki, Jacek Tabor Online Extreme Entropy Machines for Streams Classification and Active Learning, ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING vol. 403 (2016), 371-381
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

2015

Wojciech Czarnecki Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery, IEEE COMPUT INTELL M vol. 10 (3) (2015), 19-29
Andrzej Bojarski, Wojciech Czarnecki, Sabina Podlewska Robust optimization of SVM hyperparameters in the classification of bioactive compounds, J CHEMINFORMATICS vol. 7 (38) (2015), 15
Marek Śmieja, Jacek Tabor Entropy approximation in lossy source coding problem, ENTROPY-SWITZ vol. 17/5 (2015), 3400-3418
Wojciech Czarnecki, Jacek Tabor Extreme entropy machines: robust information theoretic classification, PATTERN ANAL APPL (2015), 1-18
Wojciech Czarnecki, Jacek Tabor Multithreshold Entropy Linear Classifier: Theory and Applications, EXPERT SYST APPL vol. 42 (2015), 5591-5606
Andrzej Bojarski, Wojciech Czarnecki, Sabina Smusz, Dawid Warszycki Exploiting uncertainty measures in compounds activity prediction using support vector machines, BIOORG MED CHEM LETT vol. 25 (2015), 100-105
Wojciech Czarnecki Adaptive Active Learning with Ensemble of Learners and Multiclass Problems, LECTURE NOTES IN COMPUTER SCIENCE vol. 9119 (2015), 415 - 426
Wojciech Czarnecki, Stanisław Jastrzębski Analysis of compounds activity concept learned by SVM using robust Jaccard based low-dimensional embedding, SCHEDAE INFORMATICAE vol. 24 (2015), 9-19
Wojciech Czarnecki, Rafał Józefowicz Fast optimization of Multithreshold Entropy Linear Classifier, SCHEDAE INFORMATICAE vol. 23 (2015), 57–67
Kajetan Dziedzich, Piotr Kijanka, Łukasz Pieczonka, Wiesław J. Staszewski, Konrad Żołna Statistical Analysis for Piezo-based Structural Damage Detection using Enhanced Nonlinearcrack-wave Interactions, PROCEEDINGS OF 7TH ECCOMAS THEMATIC CONFERENCE ON SMART STRUCTURES AND MATERIALS SMART 2015 vol. IDMEC 2015 (2015), 10
Andrzej Bojarski, Wojciech Czarnecki, Sabina Podlewska Extremely Randomized Machine Learning Methods for Compound Activity Prediction, MOLECULES vol. 20 (11) (2015), 20107-20117
Marek Śmieja, Jacek Tabor Spherical Wards clustering and generalized Voronoi diagrams, PROCEEDING OF IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS vol. 36678 (2015), 10
Marek Śmieja, Magdalena Wiercioch Mixture of metrics optimization for machine learning problems, SCHEDAE INFORMATICAE vol. 24 (2015), 133-142
Wojciech Czarnecki, Krzysztof Rataj Compounds Activity Prediction in Large Imbalanced Datasets with Substructural Relations Fingerprint and EEM, IEEE TRUSTCOM/BIGDATASE/ISPA PROCEEDINGS vol. 2 (2015), 192-197
Phong B. Dao, Tomasz Barszcz, Wiesław J. Staszewski, Konrad Żołna Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines, MATH PROBL ENG vol. 2015 (2015), 11
W Czarnecki, Renyi's quadratic entropy in classification, (2015)
Marek Śmieja, Jacek Tabor, Magdalena Wiercioch 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
Wojciech Czarnecki, Stanisław Jastrzębski, Sabina Podlewska, Igor Sieradzki Active Learning of Compounds Activity - Towards Scientifically Sound Simulation of Drug Candidates Identification, PROCEEDINGS OF 2ND WORKSHOP ON MACHINE LEARNING IN LIFE SCIENCES (2015), 40-51
Wojciech Czarnecki, Rafał Józefowicz Neural Connectivity Reconstruction from Calcium Imaging Signal using Random Forest with Topological Features, JMLR: WORKSHOP AND CONFERENCE PROCEEDINGS vol. 46 (2015), 67-76
Wojciech Czarnecki, Rafał Józefowicz, Jacek Tabor Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE vol. 9284 (2015), 52-67
Wojciech Czarnecki On the Consistency of Multithreshold Entropy Linear Classifier, SCHEDAE INFORMATICAE vol. 24 (2015), 123-132
Marek Śmieja Weighted approach to general entropy function, IMA J MATH CONTROL I vol. 32/2 (2015), 329-327

2014

Wojciech Czarnecki, Jacek Tabor Two ellipsoid Support Vector Machines, EXPERT SYST APPL vol. 41 (18) (2014), 8211-8224
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
Krzysztof Misztal, Przemysław Spurek, Marek Śmieja Subspaces Clustering Approach to Lossy Image Compression, LECTURE NOTES IN COMPUTER SCIENCE vol. 8838 (2014), 571-579
S Smusz, W Czarnecki, D Warszycki, AJ Bojarski, Uncertainty of the in vitro experiments in the construction of predictive models, GPCR Spring Conference 2014 (2014)
WM Czarnecki, InFeST–ImageJ Plugin for Rapid Development of Image Segmentation Pipelines, Man-Machine Interactions 3, 283-290 (2014)
Wojciech Czarnecki, Krzysztof Szarzyński, Andrzej Wójtowicz Designing a competition for autonomous robots with a restricted set of sensors with case study of LEGO NXT, JOURNAL OF AUTOMATION, MOBILE ROBOTICS & INTELLIGENT SYSTEMS - JAMRIS (2014), 76-81
Marek Śmieja, Jacek Tabor Renyi entropy dimension of the mixture of measures, PROCEEDINGS OF SCIENCE AND INFORMATION CONFERENCE (2014), 685-689
Wojciech Czarnecki, Igor Podolak Adaptive Active Learning as a Multi-armed Bandit Problem, FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS vol. 263 (2014), 989-990
WM Czarnecki, J Tabor, Cluster based RBF Kernel for Support Vector Machines, arXiv preprint arXiv:1408.2869 (2014)
W Łużny, W Czarnecki, Application of genetic algorithms to model the structure of molecular crystals., Polimery 59 (2014)

2013

Marek Śmieja, Jacek Tabor Image segmentation with use of cross-entropy clustering, ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING vol. 226 (2013), 403-409
Wojciech Czarnecki, Igor Podolak Machine Learning with Known Input Data Uncertainty Measure, LECTURE NOTES IN COMPUTER SCIENCE (2013), 379-388
WM Czarnecki, K Szarzyński, A Wójtowicz, ʥ S C S ʥ LEGO NXT, (2013)

2012

Marek Śmieja, Jacek Tabor Entropy of the mixture of source and entropy dimension, IEEE T INFORM THEORY vol. 58(5) (2012), 2719-2728
Z Vetulani, E Geoffrois, W Czarnecki, B Kochanowski, Language Resources for Public Security Applications: Needs and Specificities, Language Resources and Evaluation Conference 2011 - Language Resources for… (2012)
Wojciech Czarnecki Multilayer Neural Networks with Receptive Fields as a Model for the Neuron Reconstruction Problem, LECTURE NOTES IN COMPUTER SCIENCE (2012), 242-250

2010

W Czarnecki, S Gburek, M Smiatacz, Fast distance vector field extraction for facial feature detection, International Conference on Computer Vision and Graphics, 300-307 (2010)

Contact Us

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