Marek Śmieja
Title
Cited by
Cited by
Year
Processing of missing data by neural networks
M Smieja, Ł Struski, J Tabor, B Zieliński, P Spurek
arXiv preprint arXiv:1805.07405, 2018
492018
Hypernetwork functional image representation
S Klocek, Ł Maziarka, M Wołczyk, J Tabor, J Nowak, M Śmieja
International Conference on Artificial Neural Networks, 496-510, 2019
192019
Generalized RBF kernel for incomplete data
M Śmieja, Ł Struski, J Tabor, M Marzec
Knowledge-Based Systems 173, 150-162, 2019
192019
Constrained clustering with a complex cluster structure
M Śmieja, M Wiercioch
Advances in Data Analysis and Classification 11 (3), 493-518, 2017
172017
Entropy of the Mixture of Sources and Entropy Dimension
M Smieja, J Tabor
Information Theory, IEEE Transactions on, 1-1, 2011
172011
A classification-based approach to semi-supervised clustering with pairwise constraints
M Śmieja, Ł Struski, MAT Figueiredo
Neural Networks 127, 193-203, 2020
152020
Geometric graph convolutional neural networks
P Spurek, T Danel, J Tabor, M Smieja, L Struski, A Slowik, L Maziarka
arXiv preprint arXiv:1909.05310, 2019
13*2019
Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction
M Śmieja, D Warszycki
PLoS ONE 11 (1), e0146666, 2016
122016
Asymmetric Clustering Index in a Case Study of 5-HT1A Receptor Ligands
M Śmieja, D Warszycki, J Tabor, AJ Bojarski
PloS one 9 (7), e102069, 2014
112014
Semi-supervised cross-entropy clustering with information bottleneck constraint
M Śmieja, BC Geiger
Information Sciences 421, 254-271, 2017
92017
Weighted approach to general entropy function
M Śmieja
IMA Journal of Mathematical Control and Information 32 (2), 329-341, 2015
92015
Spherical Wards clustering and generalized Voronoi diagrams
M Śmieja, J Tabor
Data Science and Advanced Analytics (DSAA) 36678, 10, 2015
82015
Efficient mixture model for clustering of sparse high dimensional binary data
M Śmieja, K Hajto, J Tabor
Data Mining and Knowledge Discovery 33 (6), 1583-1624, 2019
72019
Fast independent component analysis algorithm with a simple closed-form solution
P Spurek, J Tabor, M Śmieja
Knowledge-Based Systems 161, 26-34, 2018
72018
Semi-supervised discriminative clustering with graph regularization
M Śmieja, O Myronov, J Tabor
Knowledge-Based Systems 151, 24-36, 2018
72018
SVM with a neutral class
M Śmieja, J Tabor, P Spurek
Pattern Analysis and Applications 22 (2), 573-582, 2019
62019
R package cec
P Spurek, K Kamieniecki, J Tabor, K Misztal, M Śmieja
Neurocomputing 237, 410-413, 2017
62017
Image segmentation with use of cross-entropy clustering
M Śmieja, J Tabor
Proceedings of the 8th International Conference on Computer Recognition …, 2013
62013
Semi-supervised model-based clustering with controlled clusters leakage
M Śmieja, Ł Struski, J Tabor
Expert Systems with Applications 85, 146-157, 2017
52017
Can auto-encoders help with filling missing data?
M Śmieja, M Kołomycki, Ł Struski, M Juda, MAT Figueiredo
ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020
42020
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