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 Wojciech M. Czarnecki
mgr Stanisław Jastrzębski mgr Damian Leśniak mgr Maciej Mikulski mgr Agnieszka Pocha mgr Igor Sieradzki mgr Magdalena Wiercioch mgr Konrad Żołna mgr Jakub Chłędowski mgr Piotr Kowenzowski mgr Łukasz Maziarka

2018

Przemysław Spurek, Marek Śmieja, Jacek Tabor SVM with a neutral class, PATTERN ANAL APPL (2018), 17

2017

Bernhard C. Geiger, Marek Śmieja Semi-supervised cross-entropy clustering with information bottleneck constraint, INFORM SCIENCES vol. 421 (2017), 245-271
Łukasz Struski, Marek Śmieja, Jacek Tabor Semi-supervised model-based clustering with controlled clusters leakage, EXPERT SYST APPL vol. 85 (2017), 146-157
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
M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ..., Reinforcement learning with unsupervised auxiliary tasks, ICLR 2017 (2017)
WM Czarnecki, S Osindero, M Jaderberg, G Świrszcz, R Pascanu, Sobolev Training for Neural Networks, NIPS 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)
WM Czarnecki, G Świrszcz, M Jaderberg, S Osindero, O Vinyals, ..., Understanding Synthetic Gradients and Decoupled Neural Interfaces, ICML 2017 (2017)
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),
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
Wojciech Czarnecki, Stanisław Jastrzębski, Damian Leśniak How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks, ARXIV (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)
K Janocha, WM Czarnecki, On Loss Functions for Deep Neural Networks in Classification, arXiv preprint arXiv:1702.05659 (2017)
M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ..., Population Based Training of Neural Networks, arXiv preprint arXiv:1711.09846 (2017)
P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ..., Value-Decomposition Networks For Cooperative Multi-Agent Learning, arXiv preprint arXiv:1706.05296 (2017)

2016

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),
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
G Swirszcz, WM Czarnecki, R Pascanu, Local minima in training of neural networks, arXiv preprint arXiv:1611.06310 (2016)
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
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

2015

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
Andrzej Bojarski, Wojciech Czarnecki, Sabina Podlewska Robust optimization of SVM hyperparameters in the classification of bioactive compounds, J CHEMINFORMATICS vol. 7 (38) (2015), 15
Wojciech Czarnecki, Jacek Tabor Multithreshold Entropy Linear Classifier: Theory and Applications, EXPERT SYST APPL vol. 42 (2015), 5591-5606
Marek Śmieja, Jacek Tabor Entropy approximation in lossy source coding problem, ENTROPY-SWITZ vol. 17/5 (2015), 3400-3418
Wojciech Czarnecki Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery, IEEE COMPUT INTELL M vol. 10 (3) (2015), 19-29
Wojciech Czarnecki, Jacek Tabor Extreme entropy machines: robust information theoretic classification, PATTERN ANAL APPL (2015), 1-18
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, 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
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 Adaptive Active Learning with Ensemble of Learners and Multiclass Problems, LECTURE NOTES IN COMPUTER SCIENCE vol. 9119 (2015), 415 - 426
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
Marek Śmieja Weighted approach to general entropy function, IMA J MATH CONTROL I vol. 32/2 (2015), 329-327
Marek Śmieja, Magdalena Wiercioch Mixture of metrics optimization for machine learning problems, SCHEDAE INFORMATICAE (2015),
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 On the Consistency of Multithreshold Entropy Linear Classifier, SCHEDAE INFORMATICAE vol. 24 (2015), 123-132
Andrzej Bojarski, Wojciech Czarnecki, Sabina Podlewska Extremely Randomized Machine Learning Methods for Compound Activity Prediction, MOLECULES vol. 20 (11) (2015), 20107-20117
Wojciech Czarnecki, Rafał Józefowicz Fast optimization of Multithreshold Entropy Linear Classifier, SCHEDAE INFORMATICAE vol. 23 (2015), 57–67
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, 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
Marek Śmieja Mixture of metrics optimization for machine learning problems, SCHEDAE INFORMATICAE vol. 24 (2015), 133-142
W Czarnecki, Renyi's quadratic entropy in classification, (2015)

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
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)
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, InFeST–ImageJ Plugin for Rapid Development of Image Segmentation Pipelines, Man-Machine Interactions 3, 283-290 (2014)
WM Czarnecki, J Tabor, Cluster based RBF Kernel for Support Vector Machines, arXiv preprint arXiv:1408.2869 (2014)
Krzysztof Misztal, Przemysław Spurek, Marek Śmieja Subspaces Clustering Approach to Lossy Image Compression, LECTURE NOTES IN COMPUTER SCIENCE vol. 8838 (2014), 571-579

2013

Igor Podolak, Adam Roman Theoretical foundations and practical results for a hierarchical classifier with overlapping clusters, COMPUT INTELL-US (2013), 357-388
Stanisław Jastrzębski, Igor Podolak Density Invariant Detection of Osteoporosis Using Growing Neural Gas, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013 vol. Springer International Publishing (2013), 629-638
Marek Śmieja, Jacek Tabor Image segmentation with use of cross-entropy clustering, ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING vol. 226 (2013), 403-409
IT Podolak, A Roman, Theoretical foundations and experimental results for a hierarchical classifier with overlapping clusters, Computational Intelligence 29 (2), 357-388 (2013)
Wojciech Czarnecki, Igor Podolak Machine Learning with Known Input Data Uncertainty Measure, LECTURE NOTES IN COMPUTER SCIENCE (2013), 379-388

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)
Dariusz Jędrzejczyk, Igor Podolak, Adam Roman Application of Hierarchical Classifier to Minimal Synchronizing Word problem, LECTURE NOTES IN COMPUTER SCIENCE vol. 7267 (2012), 421-429
IT Podolak, Hierarchiczny klasyfikator z nakładającymi się grupami klas, Wydawnictwo Uniwersytetu Jagiellońskiego WUJ (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

2011

Igor Podolak, Adam Roman Fusion of supervised and unsupervised training methods for a multi-class classification problem, PATTERN ANAL APPL vol. 14(4) (2011), 395-413
Stanisław Brodowski, Igor Podolak Hierarchical Estimator, EXPERT SYST APPL vol. 38 (2011), 12237–12248
IT Podolak, A Roman, CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem, Pattern Analysis and Applications 14 (4), 395-413 (2011)
Igor Podolak, Adam Roman Risk function estimation for subproblems in a hierarchical classifier, PATTERN RECOGN LETT vol. 32(15) (2011), 2136-2142
Agnieszka Mielniczek (Deszyńska), Igor Podolak, Adam Roman On the number of clusterings in a hierarchical classification model with overlapping clusters, SCHEDAE INFORMATICAE vol. 20 (2011), 137-157
Igor Podolak, Adam Roman Risk estimation for hierarchical classifier, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE vol. 6678 (2011), 156-163
K Bartocha, IT Podolak, Classifier Ensembles for Virtual Concept Drift–The DEnBoost Algorithm, International Conference on Hybrid Artificial Intelligence Systems, 164-171 (2011)

2009

Igor Podolak, Adam Roman A new notion of weakness in classification theory vol. 57 (2009), "Advances in Intelligent and Soft Computing", Springer Verlag (połaczony z Kluwer Academic Publishing)
IT Podolak, K Bartocha, A Hierarchical Classifier with Growing Neural Gas Clustering., ICANNGA, 283-292 (2009)
Kamil Bartocha, Igor Podolak Hierarchical Classifier with Growing Neural Gas clustering, LECTURE NOTES IN COMPUTER SCIENCE vol. 5495 (2009), 283-292

2008

Igor Podolak Hierarchical classifier with overlapping class groups, EXPERT SYST APPL vol. 1 (2008), 673-682
Bartosz Bierkowski, Piotr Kalita, Igor Podolak, Adam Roman Algorithm for intelligent prediction of requests in business systems, LECTURE NOTES IN COMPUTER SCIENCE vol. 4910 (2008), 696-707

2007

IT Podolak, Hierarchical rules for a hierarchical classifier, International Conference on Adaptive and Natural Computing Algorithms, 749-757 (2007)

2005

H Telega, I Podolak, Hill Crunching Clustered Genetic Search and its Improvements, Wyższa Szkoła Biznesu–National-Louis University (2005)

2004

A Bielecki, IT Podolak, M Bielecka, Neuronal algorithm of nonlinear regression, Schedae Informaticae 13 (2004)
I Podolak, U Demšar, Discovering structure in geographical metadata, Proceedings of the 12th conference in Geoinformatics, 1-7 (2004)

2003

IT Podolak, A Bielecki, A neural system of phonematic transformation, TASK QUARTERLY 7 (1), 115-130 (2003)
BA Podolak I.T., A neuronal system for phonematic transformation, TASK Quaterly 7 (1), 115-130 (2003)

2002

D Xi, IT Podolak, SW Lee, Facial component extraction and face recognition with support vector machines, Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE… (2002)

2001

A Bielecki, M Bielecka, IT Podolak, Symulacja pewnego nieliniowego modelu ekonometrycznego przy pomocy sieci neuronowych, Przegląd Statystyczny 48 (1-2), 151-159 (2001)

2000

LSW Podolak I.T., Finding input symbol representations for neural networks, 7th Int. Conf. on Neural Informations Processing, 1174-1179 (2000)
IT Podolak, SW Lee, A Bielecki, E Majkut, A hybrid neural system for phonematic transformation, Pattern Recognition, 2000. Proceedings. 15th International Conference on 2… (2000)

1999

IT Podolak, Feedforward neural network's sensitivity to input data representation, Computer physics communications 117 (1-2), 181-188 (1999)

1998

IT Podolak, Functional graph model of a neural network, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28… (1998)

1996

A Bielecki, IT Podolak, J Wosiek, E Majkut, Phonematic translation of polish texts by the neural network, ACTA PHYSICA POLONICA SERIES B 27, 2253-2264 (1996)

Contact Us

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