Journal papers

Marek Śmieja, Maciej Wołczyk, Jacek Tabor, Bernhard C. Geiger
SeGMA: SemiSupervised Gaussian Mixture Autoencoder,
IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2020.3016221, pp. 12, 2020

Marek Śmieja, Łukasz Struski, Mario A. T. Figueiredo
A ClassificationBased Approach to SemiSupervised Clustering with Pairwise Constraints,
Neural Networks, 127, pp. 193203, 2020

Łukasz Struski, Marek Śmieja, Jacek Tabor
Pointed subspace approach to incomplete data,
Journal of Classification, 37, pp.4257, 2020

Marek Śmieja, Krzysztof Hajto, Jacek Tabor
Efficient mixture model for clustering of sparse high dimensional binary data,
Data Mining and Knowledge Discovery, 33/6, pp. 15831624, 2019

Marek Śmieja, Łukasz Struski, Jacek Tabor, Mateusz Marzec
Generalized RBF kernel for incomplete data,
KnowledgeBased Systems, 173, pp. 150162, 2019

Łukasz Struski, Przemysław Spurek, Jacek Tabor, Marek Śmieja
Projected memory clustering,
Pattern Recognition Letters, 123, pp. 915, 2019

Marek Śmieja, Jacek Tabor, Przemysław Spurek
SVM with a neutral class,
Pattern Analysis and Applicatins, 22/2, pp. 573582, 2019

Przemysław Spurek, Jacek Tabor, Łukasz Struski, Marek Śmieja
Fast Independent Component Analysis algorithm with a simple closedform solution,
KonwledgeBased Systems, 161, pp. 2634, 2018

Marek Śmieja, Oleksandr Myronov, Jacek Tabor
Semisupervised discriminative clustering with graph regularization,
KonwledgeBased Systems, 151, pp. 2436, 2018

Marek Śmieja, Bernhard C. Geiger
Semisupervised crossentropy clustering with information bottleneck constraint,
Information Sciences, 421, pp. 245271, 2017

Marek Śmieja, Łukasz Struski, Jacek Tabor
Semisupervised modelbased clustering with controlled clusters leakage,
Expert Systems with Applications, 85, pp. 146157, 2017

Marek Śmieja, Magdalena Wiercioch
Constrained clustering with a complex cluster structure,
Advances in Data Analysis and Classification, 11/3, pp. 493518, 2017

Dawid Warszycki, Marek Śmieja, Rafał Kafel
Practical application of the Average Information Content Maximization (AICMAX) algorithm – selection of the most important structural features for serotonin receptor ligands,
Molecular Diversity, 21/2, pp. 407–412, 2017

Przemysław Spurek, Konrad Kamieniecki, Jacek Tabor, Krzysztof Misztal, Marek Śmieja
R Package CEC,
Neurocomputing, 237, pp. 410413, 2017,

Marek Śmieja, Dawid Warszycki
Average Information Content Maximization  a new approach for fingerprint hybridization and reduction,
PLoS ONE, 11/1, pp. e0146666, 2016

Marek Śmieja, Jacek Tabor
Entropy approximation in lossy source coding problem,
Entropy, 17/5, pp. 34003418, 2015

Marek Śmieja
Weighted approach to general entropy function,
IMA Journal of Mathematical Control and Information, 32/2, pp. 329327, 2015

Marek Śmieja, Dawid Warszycki, Jacek Tabor, Andrzej Bojarski
Asymmetric Clustering Index in a case study of 5HT1A receptor ligands,
PLoS ONE, 9/7, pp. e102069, 2014

Marek Śmieja, Jacek Tabor,
Entropy of the mixture of sources and entropy dimension,
IEEE Transactions on Information Theory, 58/5, pp. 27192728, 2012,
Conference papers

Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mario A. T. Figueiredo
Iterative Imputation of Missing Data using Autoencoder Dynamics,
International Conference on Neural Information Processing (ICONIP 2020),
Lecture Notes on Computer Science, pp. 12, 2020

Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka
Processing of incomplete images by (graph) convolutional neural networks,
International Conference on Neural Information Processing (ICONIP 2020),
Lecture Notes on Computer Science, pp. 12, 2020

Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka
Spatial Graph Convolutional Networks,
International Conference on Neural Information Processing (ICONIP 2020),
Communications in Computer and Information Science, pp. 8, 2020

Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
Estimating conditional density of missing values using deep Gaussian mixture model,
International Conference on Neural Information Processing (ICONIP 2020),
Lecture Notes on Computer Science, pp. 12, 2020

Paweł Morawiecki, Przemysław Spurek, Marek Śmieja, Jacek Tabor
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models,
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020),
accepted, pp. 6, 2020

Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, Marek Śmieja
Hypernetwork functional image representation,
International Conference on Artificial Neural Networks (ICANN 2019),
Lecture Notes in Computer Science , pp. 496510, 2019

Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek
Set Aggregation Network as a Trainable Pooling Layer,
International Conference on Neural Information Processing (ICONIP 2019),
Lecture Notes in Computer Science , pp. 419431, 2019

Marek Śmieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek
Processing of missing data by neural networks,
Advances in Neural Information Processing Systems 31 (NeurIPS 2018),
electronic proceedings, pp. 27192729, 2018

Łukasz Struski, Marek Śmieja, Bartosz Zieliński, Jacek Tabor
Regression SVM for incomplete data,
International Conference on Theoretical Foundations of Machine Learning (TFML 2017),
published in Schedae Informaticae, pp.13, 2017

Marek Śmieja, Szymon Nakoneczny, Jacek Tabor
Fast entropy clustering of sparse high dimensional binary data,
IEEE International Joint Conference on Neural Networks (IJCNN 2016),
published in IEEE conference proceedings, pp. 23972404, 2016

Marek Śmieja, Jacek Tabor
Spherical Wards clustering and generalized Voronoi diagrams,
IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015),
published in IEEE conference proceedings, pp. 110, 36678 2015

Magdalena Wiercioch, Marek Śmieja
Mixture of metrics optimization for machine learning problems,
International Conference on Theoretical Foundations of Machine Learning (TFML 2015),
published in Schedae Informaticae, 24, pp.7988, 2015

Przemysław Spurek, Marek Śmieja, Krzysztof Misztal
Subspaces clustering approach to lossy image compression,
International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2014),
published in Lecture Notes in Computer Science, 8838, pp. 571579, 2014

Marek Śmieja, Jacek Tabor
Renyi entropy dimension of the mixture of measures,
Science and Information Conference (SAI 2014),
published in IEEE conference proceedings, pp. 685  689, 2014

Marek Śmieja, Jacek Tabor
Image segmentation with use of crossentropy clustering,
International Conference on Computer Recognition Systems (CORES 2013),
published in Advances in Intelligent Systems and Computing, 226, pp. 403409, 2013,
Workshop materials

Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka
Processing of incomplete images by (graph) convolutional neural networks,
ICML Workshop on the Art of Learning with Missing Values (Artemiss 2020),
available online, pp. 5, 2020

Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
Estimating conditional density of missing values using deep Gaussian mixture model,
ICML Workshop on the Art of Learning with Missing Values (Artemiss 2020),
available online, pp. 7, 2020

Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor
Flowbased SVDD for anomaly detection,
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+ 2020),
available online, pp. 4, 2020

Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mário A. T. Figueiredo
Can autoencoders help with filling missing data?,
ICLR workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq 2020),
available online, pp. 6, 2020

Maciej Wołczyk, Jacek Tabor, Marek Śmieja, Szymon Maszke
BiologicallyInspired Spatial Neural Networks,
NeurIPS workshop on Real Neurons & Hidden Units (NeuroAI 2019),
available online, pp. 5, 2019

Szymon Nakoneczny, Marek Śmieja
Natural language processing methods in biological activity prediction,
ECML PKDD workshop on Machine Learning and Life Science (MLLS 2016)
published by Wroclaw University of Technology, pp. 2536, 2016

Magdalena Wiercioch, Marek Śmieja, Jacek Tabor
Probability Index of Metric Correspondence as a measure of visualization reliability,
ECML PKDD workshop on Machine Learning and Life Science (MLLS 2015),
published by Wroclaw University of Technology, pp. 1627, 2015