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\n  \n 2025\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n VisTabNet: Adapting Vision Transformers for Tabular Data.\n \n \n \n\n\n \n Wydmański, W.; Movsum-zada, U.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n In SIAM Conference on Data Mining (SDM 2025), pages 12, 2025. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wydmanski2025vistab,\n  title={VisTabNet: Adapting Vision Transformers for Tabular Data},\n  author={Wydmański, Witold and Movsum-zada, Ulvi and Tabor, Jacek and {\\'S}mieja, Marek},\n  booktitle={SIAM Conference on Data Mining (SDM 2025)},\n  pages=12,\n  year={2025}\n}\n\n
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\n  \n 2024\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Multi-Label Conditional Generation from Pre-trained Models.\n \n \n \n \n\n\n \n Proszewska, M.; Wołczyk, M.; Zieba, M.; Wielopolski, P.; Maziarka, Ł.; and Śmieja, M.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 46(9): 6185-6198. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"Multi-LabelPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{proszewska2024mlp,\n  title={Multi-Label Conditional Generation from Pre-trained Models},\n  author={Proszewska, Magdalena, and Wo\\l{}czyk, Maciej, and Zieba, Maciej, and Wielopolski, Patryk, and Maziarka, \\L{}ukasz, and \\'Smieja, Marek},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n  volume={46},\n  number={9},\n  pages={6185-6198},\n  year={2024},\n  publisher={IEEE},\n  doi={10.1109/TPAMI.2024.3382008}, url={https://www.researchgate.net/publication/379307428_Multi-Label_Conditional_Generation_From_Pre-Trained_Models#fullTextFileContent}\n}\n\n
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\n \n\n \n \n \n \n \n \n Face Identity-Aware Disentanglement in StyleGAN.\n \n \n \n \n\n\n \n Suwała, A.; Wójcik, B.; Proszewska, M.; Tabor, J.; Spurek, P.; and Śmieja, M.\n\n\n \n\n\n\n In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024), pages 5222–5231, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"FacePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{suwala2024face,\n  title={Face Identity-Aware Disentanglement in StyleGAN},\n  author={Suwa{\\l}a, Adrian and W{\\'o}jcik, Bartosz and Proszewska, Magdalena and Tabor, Jacek and Spurek, Przemys{\\l}aw and {\\'S}mieja, Marek},\n  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)},\n  pages={5222--5231},\n  year={2024}, url={https://openaccess.thecvf.com/content/WACV2024/papers/Suwala_Face_Identity-Aware_Disentanglement_in_StyleGAN_WACV_2024_paper.pdf}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN.\n \n \n \n \n\n\n \n Bedyhaj, A.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024), pages 118--130, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"StyleAutoEncoderPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{bedyhaj2024styleae,\n  title={StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN},\n  author={Bedyhaj, Andrzej and Tabor, Jacek and {\\'S}mieja, Marek},\n  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024)},\n  pages=118--130,\n  year={2024},\n  url={https://link.springer.com/chapter/10.1007/978-981-97-2253-2_10}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Augmentation-aware Self-supervised Learning with Conditioned Projector.\n \n \n \n \n\n\n \n Przewięźlikowski, M.; Pyla, M.; Zieliński, B.; Twardowski, B.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n Knowledge-Based Systems (KnoSys). 2024.\n \n\n\n\n
\n\n\n\n \n \n \"Augmentation-awarePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{przew2024cassle,\n  title={Augmentation-aware Self-supervised Learning with Conditioned Projector},\n  author={Przewięźlikowski, Marcin and Pyla, Mateusz and Zieliński, Bartosz and Twardowski, Bartłomiej and Tabor, Jacek and Śmieja, Marek},\n  journal={Knowledge-Based Systems (KnoSys)},\n  year={2024},\n  url={https://arxiv.org/pdf/2306.06082},\n  doi={https://doi.org/10.1016/j.knosys.2024.112572}\n}\n\n\n
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\n \n\n \n \n \n \n \n A deep cut into Split Federated Self-Supervised Learning.\n \n \n \n\n\n \n Przewięźlikowski, M.; Osial, M.; Śmieja, M.; and Zielinski, B.\n\n\n \n\n\n\n In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2024), pages 16, 2024. \n \n\n\n\n
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@inproceedings{przew2024adeep,\n  title={A deep cut into Split Federated Self-Supervised Learning},\n  author={Przewi{\\k{e}}{\\'z}likowski, Marcin and Osial, Marcin and {\\'S}mieja, Marek and Zielinski, Bartosz},\n  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2024)},\n  pages=16,\n  year={2024}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.\n \n \n \n \n\n\n \n Jamrozik, E.; Śmieja, M.; and Podlewska, S.\n\n\n \n\n\n\n Journal of Chemical Information and Modeling (JCIM), 64: 1425–1432. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"ADMET-PrInt:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jamrozik2024admet,\n  title={ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation},\n  author={Jamrozik, Ewelina and Śmieja, Marek and Podlewska, Sabina},\n  journal={Journal of Chemical Information and Modeling (JCIM)},\n  volume={64},\n  pages={1425--1432},\n  year={2024},\n  publisher={ACS Publications},\n  url={https://pubs.acs.org/doi/abs/10.1021/acs.jcim.3c02038},\n  doi={https://doi.org/10.1021/acs.jcim.3c02038}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n RegFlow: Probabilistic flow-based regression for future prediction.\n \n \n \n \n\n\n \n Zięba, M.; Przewięźlikowski, M.; Śmieja, M.; Tabor, J.; Trzcinski, T.; and Spurek, P.\n\n\n \n\n\n\n In Asian Conference on Intelligent Information and Database Systems (ACIIDS 2024), pages 12, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"RegFlow:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{przew2024regflow,\n  title={RegFlow: Probabilistic flow-based regression for future prediction},\n  author={Zi{\\k{e}}ba, Maciej and Przewi{\\k{e}}{\\'z}likowski, Marcin and {\\'S}mieja, Marek and Tabor, Jacek and Trzcinski, Tomasz and Spurek, Przemys{\\l}aw},\n  booktitle={Asian Conference on Intelligent Information and Database Systems (ACIIDS 2024)},\n  pages=12,\n  year={2024},\n  url={https://arxiv.org/abs/2011.14620}\n}\n\n
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\n  \n 2023\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets.\n \n \n \n \n\n\n \n Wydmanski, W.; Bulenok, O.; and Śmieja, M.\n\n\n \n\n\n\n In IEEE International Conference on Data Science and Advanced Analytics (DSAA 2023), pages 1–9, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"HyperTab:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wydmanski2023hypertab,\n  title={HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets},\n  author={Wydmanski, Witold and Bulenok, Oleksii and {\\'S}mieja, Marek},\n  booktitle={IEEE International Conference on Data Science and Advanced Analytics (DSAA 2023)},\n  pages={1--9},\n  year={2023},\n  url={https://arxiv.org/pdf/2304.03543.pdf},\n  doi={10.1109/DSAA60987.2023.10302504}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n ChiENN: Embracing Molecular Chirality with Graph Neural Networks.\n \n \n \n \n\n\n \n Gaiński, P.; Koziarski, M.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2023), pages 36–52, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"ChiENN:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gainski2023chienn,\n  title={ChiENN: Embracing Molecular Chirality with Graph Neural Networks},\n  author={Gaiński, Piotr and Koziarski, Micha\\l{} and Tabor, Jacek and {\\'S}mieja, Marek},\n  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2023)},\n  pages={36--52},\n  year={2023},\n  url={https://arxiv.org/pdf/2307.02198.pdf},\n  doi={https://doi.org/10.1007/978-3-031-43418-1_3}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Contrastive Hierarchical Clustering.\n \n \n \n \n\n\n \n Znaleźniak, M.; Rola, P.; Kaszuba, P.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2023), pages 627–643, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"ContrastivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{znalezniak2023contrastive,\n  title={Contrastive Hierarchical Clustering},\n  author={Znale{\\'z}niak, Micha{\\l} and Rola, Przemys{\\l}aw and Kaszuba, Patryk and Tabor, Jacek and {\\'S}mieja, Marek},\n  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2023)},\n  pages={627–643},\n  year={2023},\n  url={https://arxiv.org/pdf/2303.03389.pdf},\n  doi={https://doi.org/10.1007/978-3-031-43412-9_37}\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n SONG: Self-Organizing Neural Graphs.\n \n \n \n \n\n\n \n Struski, Ł.; Danel, T.; Śmieja, M.; Tabor, J.; and Zieliński, B.\n\n\n \n\n\n\n In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023), pages 3848–3857, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"SONG:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{struski2021song,\n  title={SONG: Self-Organizing Neural Graphs},\n  author={Struski, {\\L}ukasz and Danel, Tomasz and {\\'S}mieja, Marek and Tabor, Jacek and Zieli{\\'n}ski, Bartosz},\n  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023)},\n  pages={3848--3857},\n  year={2023}, url={https://openaccess.thecvf.com/content/WACV2023/papers/Struski_SONGs_Self-Organizing_Neural_Graphs_WACV_2023_paper.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Hebbian Continual Representation Learning.\n \n \n \n \n\n\n \n Morawiecki, P.; Krutsylo, A.; Wołczyk, M.; and Śmieja, M.\n\n\n \n\n\n\n In Hawaii International Conference on System Sciences (HICSS 2023), pages 1259–1268, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"HebbianPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{morawiecki2022hebbian,\n  title={Hebbian Continual Representation Learning},\n  author={Morawiecki, Pawe{\\l} and Krutsylo, Andrii and Wo{\\l}czyk, Maciej and {\\'S}mieja, Marek},\n  booktitle={Hawaii International Conference on System Sciences (HICSS 2023)},\n  pages={1259--1268},\n  year={2023},\n  url={https://arxiv.org/pdf/2207.04874.pdf}, \n  doi={https://hdl.handle.net/10125/102785}\n}\n\n
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\n \n\n \n \n \n \n \n \n r-softmax: Generalized Softmax with Controllable Sparsity Rate.\n \n \n \n \n\n\n \n Bałazy, K.; Struski, Ł.; Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In International Conference on Computational Sciences (ICCS 2023), pages 137–145, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"r-softmax:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{balazy2023rsoftmax,\n  title={r-softmax: Generalized Softmax with Controllable Sparsity Rate},\n  author={Ba\\l{}azy, Klaudia and Struski, \\L{}ukasz and \\'Smieja, Marek and Tabor, Jacek},\n  booktitle={International Conference on Computational Sciences (ICCS 2023)},\n  pages={137--145},\n  year={2023},\n  url={https://arxiv.org/pdf/2304.05243.pdf},\n  doi={ttps://doi.org/10.1007/978-3-031-36021-3_11}\n}\n\n\n\n\n
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\n \n\n \n \n \n \n \n \n Zero time waste in pre-trained early exit neural networks.\n \n \n \n \n\n\n \n Wójcik, B.; Przewiȩźlikowski, M.; Szatkowski, F.; Wołczyk, M.; Bałazy, K.; Krzepkowski, B.; Podolak, I.; Tabor, J.; Śmieja, M.; and Trzciński, T.\n\n\n \n\n\n\n Neural Networks, 168: 580–601. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ZeroPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wojcik2023zero,\n  title={Zero time waste in pre-trained early exit neural networks},\n  author={W{\\'o}jcik, Bartosz and Przewiȩ{\\'z}likowski, Marcin and Szatkowski, Filip and Wo{\\l}czyk, Maciej and Ba{\\l}azy, Klaudia and Krzepkowski, Bart{\\l}omiej and Podolak, Igor and Tabor, Jacek and {\\'S}mieja, Marek and Trzci{\\'n}ski, Tomasz},\n  journal={Neural Networks},\n  volume={168},\n  pages={580--601},\n  year={2023},\n  publisher={Elsevier},\n  doi={10.1016/j.neunet.2023.10.003},\n  url={https://doi.org/10.1016/j.neunet.2023.10.003}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n PluGeN: Multi-Label Conditional Generation From Pre-Trained Models.\n \n \n \n \n\n\n \n Wołczyk, M.; Proszewska, M.; Maziarka, Ł.; Zieba, M.; Wielopolski, P.; Kurczab, R.; and Śmieja, M.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI 2022), volume 36, pages 8647–8656, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"PluGeN:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wolczyk2022plugen,\n  title={PluGeN: Multi-Label Conditional Generation From Pre-Trained Models},\n  author={Wo{\\l}czyk, Maciej and Proszewska, Magdalena and Maziarka, {\\L}ukasz and Zieba, Maciej and Wielopolski, Patryk and Kurczab, Rafa{\\l} and \\'Smieja, Marek},\n  booktitle={AAAI Conference on Artificial Intelligence (AAAI 2022)},\n  volume={36},\n  number={8},\n  pages={8647--8656},\n  year={2022},\n  url={https://ojs.aaai.org/index.php/AAAI/article/view/20843/20602},\n  doi={https://doi.org/10.1609/aaai.v36i8.20843}\n}\n\n
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\n \n\n \n \n \n \n \n \n MisConv: Convolutional Neural Networks for Missing Data.\n \n \n \n \n\n\n \n Przewięźlikowski, M.; Śmieja, M.; Struski, Ł.; and Tabor, J.\n\n\n \n\n\n\n In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022), pages 2060–2069, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"MisConv:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{przewikezlikowski2022misconv,\n  title={MisConv: Convolutional Neural Networks for Missing Data},\n  author={Przewi{\\k{e}}{\\'z}likowski, Marcin and {\\'S}mieja, Marek and Struski, {\\L}ukasz and Tabor, Jacek},\n  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022)},\n  pages={2060--2069},\n  year={2022}, url={https://openaccess.thecvf.com/content/WACV2022/papers/Przewiezlikowski_MisConv_Convolutional_Neural_Networks_for_Missing_Data_WACV_2022_paper.pdf}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Semi-supervised clustering via information-theoretic markov chain aggregation.\n \n \n \n \n\n\n \n Steger, S.; Geiger, B. C; and Śmieja, M.\n\n\n \n\n\n\n In ACM/SIGAPP Symposium on Applied Computing (SAC 2022), pages 1136–1139, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Semi-supervisedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{steger2022semi,\n  title={Semi-supervised clustering via information-theoretic markov chain aggregation},\n  author={Steger, Sophie and Geiger, Bernhard C and {\\'S}mieja, Marek},\n  booktitle={ACM/SIGAPP Symposium on Applied Computing (SAC 2022)},\n  pages={1136--1139},\n  year={2022},\n  url={https://arxiv.org/pdf/2112.09397.pdf},\n  doi={https://doi.org/10.1145/3477314.3507181}\n}\n\n
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\n \n\n \n \n \n \n \n \n SLOVA: Uncertainty estimation using single label one-vs-all classifier.\n \n \n \n \n\n\n \n Wójcik, B.; Grela, J.; Śmieja, M.; Misztal, K.; and Tabor, J.\n\n\n \n\n\n\n Applied Soft Computing, 126: 109219. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"SLOVA:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wojcik2022slova,\n  title={SLOVA: Uncertainty estimation using single label one-vs-all classifier},\n  author={W{\\'o}jcik, Bartosz and Grela, Jacek and {\\'S}mieja, Marek and Misztal, Krzysztof and Tabor, Jacek},\n  journal={Applied Soft Computing},\n  volume={126},\n  pages={109219},\n  year={2022},\n  doi={https://doi.org/10.1016/j.asoc.2022.109219},\n  url={https://arxiv.org/pdf/2206.13923.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n OneFlow: One-class flow for anomaly detection based on a minimal volume region.\n \n \n \n \n\n\n \n Maziarka, Ł.; Śmieja, M.; Sendera, M.; Struski, Ł.; Tabor, J.; and Spurek, P.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(11): 8508–8519. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"OneFlow:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{maziarka2021oneflow,\n  title={OneFlow: One-class flow for anomaly detection based on a minimal volume region},\n  author={Maziarka, {\\L}ukasz and {\\'S}mieja, Marek and Sendera, Marcin and Struski, {\\L}ukasz and Tabor, Jacek and Spurek, Przemys{\\l}aw},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n  volume={44},\n  number={11},\n  pages={8508--8519},\n  year={2022},\n  url={https://arxiv.org/abs/2010.03002},\n  doi={10.1109/TPAMI.2021.3108223}\n}\n\n\n
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\n  \n 2021\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n SeGMA: Semi-supervised gaussian mixture autoencoder.\n \n \n \n \n\n\n \n Śmieja, M.; Wołczyk, M.; Tabor, J.; and Geiger, B. C\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(9): 3930–3941. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"SeGMA:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2020segma,\n  title={SeGMA: Semi-supervised gaussian mixture autoencoder},\n  author={{\\'S}mieja, Marek and Wo{\\l}czyk, Maciej and Tabor, Jacek and Geiger, Bernhard C},\n  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n  volume={32},\n  number={9},\n  pages={3930--3941},\n  year={2021},\n  doi={10.1109/TNNLS.2020.3016221},\n  url={https://arxiv.org/pdf/1906.09333.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Adversarial examples detection and analysis with layer-wise autoencoders.\n \n \n \n \n\n\n \n Wójcik, B.; Morawiecki, P.; Śmieja, M.; Krzyżek, T.; Spurek, P.; and Tabor, J.\n\n\n \n\n\n\n In IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2021), pages 1322–1326, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"AdversarialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wojcik2021adversarial,\n  title={Adversarial examples detection and analysis with layer-wise autoencoders},\n  author={W{\\'o}jcik, Bartosz and Morawiecki, Pawe{\\l} and {\\'S}mieja, Marek and Krzy{\\.z}ek, Tomasz and Spurek, Przemys{\\l}aw and Tabor, Jacek},\n  booktitle={IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2021)},\n  pages={1322--1326},\n  year={2021},\n  doi={10.1109/ICTAI52525.2021.00209},\n  url={https://arxiv.org/pdf/2006.10013.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Zero Time Waste: Recycling Predictions in Early Exit Neural Networks.\n \n \n \n \n\n\n \n Wołczyk, M.; Wójcik, B.; Bałazy, K.; Podolak, I. T; Tabor, J.; Śmieja, M.; and Trzcinski, T.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NeurIPS 2021), volume 34, pages 2516–2528, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"ZeroPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wolczyk2021zero,\n  title={Zero Time Waste: Recycling Predictions in Early Exit Neural Networks},\n  author={Wo{\\l}czyk, Maciej and W{\\'o}jcik, Bartosz and Ba{\\l}azy, Klaudia and Podolak, Igor T and Tabor, Jacek and {\\'S}mieja, Marek and Trzcinski, Tomasz},\n  booktitle={Advances in Neural Information Processing Systems (NeurIPS 2021)},\n  volume={34},\n  pages={2516--2528},\n  year={2021},\n  url={https://openreview.net/pdf?id=7AiFm-cB-ac}\n}\n\n\n\n\n\n\n
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\n \n\n \n \n \n \n \n \n Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design.\n \n \n \n \n\n\n \n Warszycki, D.; Struski, Ł.; Smieja, M.; Kafel, R.; and Kurczab, R.\n\n\n \n\n\n\n Journal of Chemical Information and Modeling (JCIM), 61(10): 5054–5065. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Pharmacoprint:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{warszycki2021pharmacoprint,\n  title={Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design},\n  author={Warszycki, Dawid and Struski, {\\L}ukasz and Smieja, Marek and Kafel, Rafa{\\l} and Kurczab, Rafa{\\l}},\n  journal={Journal of Chemical Information and Modeling (JCIM)},\n  volume={61},\n  number={10},\n  pages={5054--5065},\n  year={2021},\n  doi={https://doi.org/10.1021/acs.jcim.1c00589},\n  url={https://arxiv.org/ftp/arxiv/papers/2110/2110.01339.pdf}\n}\n\n\n
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\n  \n 2020\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Pointed subspace approach to incomplete data.\n \n \n \n \n\n\n \n Struski, L.; Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n Journal of Classification, 37: 42–57. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"PointedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{struski2020pointed,\n  title={Pointed subspace approach to incomplete data},\n  author={Struski, Lukasz and {\\'S}mieja, Marek and Tabor, Jacek},\n  journal={Journal of Classification},\n  volume={37},\n  pages={42--57},\n  year={2020},\n  url={https://link.springer.com/content/pdf/10.1007/s00357-019-9304-3.pdf},\n  doi={https://doi.org/10.1007/s00357-019-9304-3}\n}\n\n
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\n \n\n \n \n \n \n \n \n Spatial graph convolutional networks.\n \n \n \n \n\n\n \n Danel, T.; Spurek, P.; Tabor, J.; Śmieja, M.; Struski, Ł.; Słowik, A.; and Maziarka, Ł.\n\n\n \n\n\n\n In International Conference on Neural Information Processing (ICONIP 2020), pages 668–675, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"SpatialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{danel2020spatial,\n  title={Spatial graph convolutional networks},\n  author={Danel, Tomasz and Spurek, Przemys{\\l}aw and Tabor, Jacek and {\\'S}mieja, Marek and Struski, {\\L}ukasz and S{\\l}owik, Agnieszka and Maziarka, {\\L}ukasz},\n  booktitle={International Conference on Neural Information Processing (ICONIP 2020)},\n  pages={668--675},\n  year={2020},\n  url={https://arxiv.org/pdf/1909.05310.pdf},\n  doi={https://doi.org/10.1007/978-3-030-63823-8_76}\n}\n\n
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\n \n\n \n \n \n \n \n \n A classification-based approach to semi-supervised clustering with pairwise constraints.\n \n \n \n \n\n\n \n Śmieja, M.; Struski, Ł.; and Figueiredo, M. A.\n\n\n \n\n\n\n Neural Networks, 127: 193–203. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2020classification,\n  title={A classification-based approach to semi-supervised clustering with pairwise constraints},\n  author={{\\'S}mieja, Marek and Struski, {\\L}ukasz and Figueiredo, M{\\'a}rio AT},\n  journal={Neural Networks},\n  volume={127},\n  pages={193--203},\n  year={2020},\n  url={https://arxiv.org/pdf/2001.06720.pdf},\n  doi={https://doi.org/10.1016/j.neunet.2020.04.017}\n}\n\n
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\n \n\n \n \n \n \n \n \n Can auto-encoders help with filling missing data?.\n \n \n \n \n\n\n \n Śmieja, M.; Kołomycki, M.; Struski, Ł.; Juda, M.; and Figueiredo, M. A.\n\n\n \n\n\n\n In ICLR Workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq 2020), pages 6, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"CanPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2020can,\n  title={Can auto-encoders help with filling missing data?},\n  author={{\\'S}mieja, Marek and Ko{\\l}omycki, Maciej and Struski, {\\L}ukasz and Juda, Mateusz and Figueiredo, M{\\'a}rio AT},\n  booktitle={ICLR Workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq 2020)},\n  pages={6},\n  year={2020},\n  url={https://openreview.net/pdf?id=C2nr-4elBV}\n}\n\n
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\n \n\n \n \n \n \n \n \n Processing of incomplete images by (graph) convolutional neural networks.\n \n \n \n \n\n\n \n Danel, T.; Śmieja, M.; Struski, Ł.; Spurek, P.; and Maziarka, Ł.\n\n\n \n\n\n\n In International Conference on Neural Information Processing (ICONIP 2020), pages 512–523, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"ProcessingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{danel2020processing,\n  title={Processing of incomplete images by (graph) convolutional neural networks},\n  author={Danel, Tomasz and {\\'S}mieja, Marek and Struski, {\\L}ukasz and Spurek, Przemys{\\l}aw and Maziarka, {\\L}ukasz},\n  booktitle={International Conference on Neural Information Processing (ICONIP 2020)},\n  pages={512--523},\n  year={2020},\n  url={https://arxiv.org/pdf/2010.13914.pdf},\n  doi={https://doi.org/10.1007/978-3-030-63833-7_43}\n}\n\n
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\n \n\n \n \n \n \n \n \n Estimating conditional density of missing values using deep gaussian mixture model.\n \n \n \n \n\n\n \n Przewięźlikowski, M.; Śmieja, M.; and Struski, Ł.\n\n\n \n\n\n\n In International Conference on Neural Information Processing (ICONIP 2020), pages 220–231, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"EstimatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{przewikezlikowski2020estimating,\n  title={Estimating conditional density of missing values using deep gaussian mixture model},\n  author={Przewi{\\k{e}}{\\'z}likowski, Marcin and {\\'S}mieja, Marek and Struski, {\\L}ukasz},\n  booktitle={International Conference on Neural Information Processing (ICONIP 2020)},\n  pages={220--231},\n  year={2020},\n  doi={https://doi.org/10.1007/978-3-030-63836-8_19},\n  url={https://arxiv.org/pdf/2010.02183.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Iterative imputation of missing data using auto-encoder dynamics.\n \n \n \n \n\n\n \n Śmieja, M.; Kołomycki, M.; Struski, Ł.; Juda, M.; and Figueiredo, M. A.\n\n\n \n\n\n\n In International Conference on Neural Information Processing (ICONIP 2020), pages 258–269, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"IterativePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2020iterative,\n  title={Iterative imputation of missing data using auto-encoder dynamics},\n  author={{\\'S}mieja, Marek and Ko{\\l}omycki, Maciej and Struski, {\\L}ukasz and Juda, Mateusz and Figueiredo, M{\\'a}rio AT},\n  booktitle={International Conference on Neural Information Processing (ICONIP 2020)},\n  pages={258--269},\n  year={2020},\n  url={pubs/AE_missing_data.pdf},\n  doi={https://doi.org/10.1007/978-3-030-63836-8_22}\n}\n\n
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\n \n\n \n \n \n \n \n \n Flow-based SVDD for anomaly detection.\n \n \n \n \n\n\n \n Sendera, M.; Śmieja, M.; Maziarka, Ł.; Struski, Ł.; Spurek, P.; and Tabor, J.\n\n\n \n\n\n\n In ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+ 2020), 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Flow-basedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{sendera2021flow,\n  title={Flow-based SVDD for anomaly detection},\n  author={Sendera, Marcin and {\\'S}mieja, Marek and Maziarka, {\\L}ukasz and Struski, {\\L}ukasz and Spurek, Przemys{\\l}aw and Tabor, Jacek},\n  booktitle={ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+ 2020)},\n  year={2020},\n  url={https://invertibleworkshop.github.io/accepted_papers/pdfs/35.pdf}\n}\n\n
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\n  \n 2019\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Efficient mixture model for clustering of sparse high dimensional binary data.\n \n \n \n \n\n\n \n Śmieja, M.; Hajto, K.; and Tabor, J.\n\n\n \n\n\n\n Data Mining and Knowledge Discovery, 33(6): 1583–1624. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2019efficient,\n  title={Efficient mixture model for clustering of sparse high dimensional binary data},\n  author={{\\'S}mieja, Marek and Hajto, Krzysztof and Tabor, Jacek},\n  journal={Data Mining and Knowledge Discovery},\n  volume={33},\n  number={6},\n  pages={1583--1624},\n  year={2019},\n  url={https://link.springer.com/content/pdf/10.1007/s10618-019-00635-1.pdf},\n  doi={https://doi.org/10.1007/s10618-019-00635-1}\n}\n\n
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\n \n\n \n \n \n \n \n \n SVM with a neutral class.\n \n \n \n \n\n\n \n Śmieja, M.; Tabor, J.; and Spurek, P.\n\n\n \n\n\n\n Pattern Analysis and Applications, 22(2): 573–582. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"SVMPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2019svm,\n  title={SVM with a neutral class},\n  author={{\\'S}mieja, Marek and Tabor, Jacek and Spurek, Przemyslaw},\n  journal={Pattern Analysis and Applications},\n  volume={22},\n  number={2},\n  pages={573--582},\n  year={2019},\n  url={https://link.springer.com/content/pdf/10.1007/s10044-017-0654-3.pdf},\n  doi={https://doi.org/10.1007/s10044-017-0654-3}\n}\n\n
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\n \n\n \n \n \n \n \n \n Generalized RBF kernel for incomplete data.\n \n \n \n \n\n\n \n Śmieja, M.; Struski, Ł.; Tabor, J.; and Marzec, M.\n\n\n \n\n\n\n Knowledge-Based Systems, 173: 150–162. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"GeneralizedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2019generalized,\n  title={Generalized RBF kernel for incomplete data},\n  author={{\\'S}mieja, Marek and Struski, {\\L}ukasz and Tabor, Jacek and Marzec, Mateusz},\n  journal={Knowledge-Based Systems},\n  volume={173},\n  pages={150--162},\n  year={2019},\n  doi={https://doi.org/10.1016/j.knosys.2019.02.034},\n  url={pubs/genRBF.pdf}  \n}\n\n
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\n \n\n \n \n \n \n \n Projected memory clustering.\n \n \n \n\n\n \n Struski, Ł.; Spurek, P.; Tabor, J.; and Śmieja, M.\n\n\n \n\n\n\n Pattern Recognition Letters, 123: 9–15. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{struski2019projected,\n  title={Projected memory clustering},\n  author={Struski, {\\L}ukasz and Spurek, Przemys{\\l}aw and Tabor, Jacek and {\\'S}mieja, Marek},\n  journal={Pattern Recognition Letters},\n  volume={123},\n  pages={9--15},\n  year={2019},\n  doi={https://doi.org/10.1016/j.patrec.2019.02.023}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fast and stable interval bounds propagation for training verifiably robust models.\n \n \n \n \n\n\n \n Morawiecki, P.; Spurek, P.; Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), pages 6, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{morawiecki2019fast,\n  title={Fast and stable interval bounds propagation for training verifiably robust models},\n  author={Morawiecki, Pawe{\\l} and Spurek, Przemys{\\l}aw and {\\'S}mieja, Marek and Tabor, Jacek},\n  booktitle={European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)},\n  pages={6},\n  year={2019},\n  url={https://www.esann.org/sites/default/files/proceedings/2020/ES2020-57.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Hypernetwork functional image representation.\n \n \n \n \n\n\n \n Klocek, S.; Maziarka, Ł.; Wołczyk, M.; Tabor, J.; Nowak, J.; and Śmieja, M.\n\n\n \n\n\n\n In International Conference on Artificial Neural Networks (ICANN 2019), pages 496–510, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"HypernetworkPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{klocek2019hypernetwork,\n  title={Hypernetwork functional image representation},\n  author={Klocek, Sylwester and Maziarka, {\\L}ukasz and Wo{\\l}czyk, Maciej and Tabor, Jacek and Nowak, Jakub and {\\'S}mieja, Marek},\n  booktitle={International Conference on Artificial Neural Networks (ICANN 2019)},\n  pages={496--510},\n  year={2019},\n  doi={https://doi.org/10.1007/978-3-030-30493-5_48},\n  url={pubs/ICANN2019.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Biologically-Inspired Spatial Neural Networks.\n \n \n \n \n\n\n \n Wołczyk, M.; Tabor, J.; Śmieja, M.; and Maszke, S.\n\n\n \n\n\n\n In NeurIPS Workshop on Real Neurons & Hidden Units (NeuroAI 2019), pages 5, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Biologically-InspiredPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wolczyk2019biologically,\n  title={Biologically-Inspired Spatial Neural Networks},\n  author={Wo{\\l}czyk, Maciej and Tabor, Jacek and {\\'S}mieja, Marek and Maszke, Szymon},\n  booktitle={NeurIPS Workshop on Real Neurons & Hidden Units (NeuroAI 2019)},\n  pages={5},\n  year={2019},\n  url={https://openreview.net/pdf?id=SyxTQ7K88S}\n}\n\n
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\n \n\n \n \n \n \n \n \n Set Aggregation Network as a Trainable Pooling Layer.\n \n \n \n \n\n\n \n Maziarka, Ł.; Śmieja, M.; Nowak, A.; Tabor, J.; Struski, Ł.; and Spurek, P.\n\n\n \n\n\n\n In International Conference on Neural Information Processing (ICONIP 2019), pages 419–431, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"SetPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{maziarka2019set,\n  title={Set Aggregation Network as a Trainable Pooling Layer},\n  author={Maziarka, {\\L}ukasz and {\\'S}mieja, Marek and Nowak, Aleksandra and Tabor, Jacek and Struski, {\\L}ukasz and Spurek, Przemys{\\l}aw},\n  booktitle={International Conference on Neural Information Processing (ICONIP 2019)},\n  pages={419--431},\n  year={2019},\n  url={https://arxiv.org/pdf/1810.01868.pdf},\n  doi={https://doi.org/10.1007/978-3-030-36711-4_35}\n}\n\n
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\n  \n 2018\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Semi-supervised discriminative clustering with graph regularization.\n \n \n \n \n\n\n \n Śmieja, M.; Myronov, O.; and Tabor, J.\n\n\n \n\n\n\n Knowledge-Based Systems, 151: 24–36. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Semi-supervisedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2018semi,\n  title={Semi-supervised discriminative clustering with graph regularization},\n  author={{\\'S}mieja, Marek and Myronov, Oleksandr and Tabor, Jacek},\n  journal={Knowledge-Based Systems},\n  volume={151},\n  pages={24--36},\n  year={2018},\n  doi={https://doi.org/10.1016/j.knosys.2018.03.019},\n  url={pubs/disc.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Fast independent component analysis algorithm with a simple closed-form solution.\n \n \n \n\n\n \n Spurek, P.; Tabor, J.; Struski, Ł.; and Śmieja, M.\n\n\n \n\n\n\n Knowledge-Based Systems, 161: 26–34. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{spurek2018fast,\n  title={Fast independent component analysis algorithm with a simple closed-form solution},\n  author={Spurek, Przemyslaw and Tabor, Jacek and Struski, \\L{}ukasz and {\\'S}mieja, Marek},\n  journal={Knowledge-Based Systems},\n  volume={161},\n  pages={26--34},\n  year={2018},\n  doi={https://doi.org/10.1016/j.knosys.2018.07.027},\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Processing of missing data by neural networks.\n \n \n \n \n\n\n \n Śmieja, M.; Struski, Ł.; Tabor, J.; Zieliński, B.; and Spurek, P.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NeurIPS 2018), volume 31, pages 2719–2729, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"ProcessingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2018processing,\n  title={Processing of missing data by neural networks},\n  author={{\\'S}mieja, Marek and Struski, {\\L}ukasz and Tabor, Jacek and Zieli{\\'n}ski, Bartosz and Spurek, Przemys{\\l}aw},\n  booktitle={Advances in Neural Information Processing Systems (NeurIPS 2018)},\n  volume={31},\n  pages={2719--2729},\n  year={2018}, url={https://proceedings.neurips.cc/paper/2018/file/411ae1bf081d1674ca6091f8c59a266f-Paper.pdf}\n}\n\n
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\n  \n 2017\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Constrained clustering with a complex cluster structure.\n \n \n \n \n\n\n \n Śmieja, M.; and Wiercioch, M.\n\n\n \n\n\n\n Advances in Data Analysis and Classification, 11(3): 493–518. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ConstrainedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2017constrained,\n  title={Constrained clustering with a complex cluster structure},\n  author={{\\'S}mieja, Marek and Wiercioch, Magdalena},\n  journal={Advances in Data Analysis and Classification},\n  volume={11},\n  number={3},\n  pages={493--518},\n  year={2017},\n  url={https://link.springer.com/content/pdf/10.1007/s11634-016-0254-x.pdf},\n  doi={https://doi.org/10.1007/s11634-016-0254-x}\n}\n\n
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\n \n\n \n \n \n \n \n \n R package CEC.\n \n \n \n \n\n\n \n Spurek, P.; Kamieniecki, K.; Tabor, J.; Misztal, K.; and Śmieja, M.\n\n\n \n\n\n\n Neurocomputing, 237: 410–413. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"RPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{spurek2017r,\n  title={R package CEC},\n  author={Spurek, Przemyslaw and Kamieniecki, Konrad and Tabor, Jacek and Misztal, Krzysztof and {\\'S}mieja, Marek},\n  journal={Neurocomputing},\n  volume={237},\n  pages={410--413},\n  year={2017},\n  url={pubs/r-cec.pdf},\n  doi={https://doi.org/10.1016/j.neucom.2016.08.118}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.\n \n \n \n \n\n\n \n Warszycki, D.; Śmieja, M.; and Kafel, R.\n\n\n \n\n\n\n Molecular Diversity, 21(2): 407–412. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"PracticalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{warszycki2017practical,\n  title={Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands},\n  author={Warszycki, Dawid and {\\'S}mieja, Marek and Kafel, Rafa{\\l}},\n  journal={Molecular Diversity},\n  volume={21},\n  number={2},\n  pages={407--412},\n  year={2017},\n  url={https://link.springer.com/content/pdf/10.1007/s11030-017-9729-8.pdf},\n  doi={https://doi.org/10.1007/s11030-017-9729-8}\n}\n\n
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\n \n\n \n \n \n \n \n \n Semi-supervised model-based clustering with controlled clusters leakage.\n \n \n \n \n\n\n \n Śmieja, M.; Struski, Ł.; and Tabor, J.\n\n\n \n\n\n\n Expert Systems with Applications, 85: 146–157. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Semi-supervisedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2017semi,\n  title={Semi-supervised model-based clustering with controlled clusters leakage},\n  author={{\\'S}mieja, Marek and Struski, {\\L}ukasz and Tabor, Jacek},\n  journal={Expert Systems with Applications},\n  volume={85},\n  pages={146--157},\n  year={2017},\n  url={pubs/c3l.pdf},\n  doi={https://doi.org/10.1016/j.eswa.2017.05.032}\n}\n\n
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\n \n\n \n \n \n \n \n \n Semi-supervised cross-entropy clustering with information bottleneck constraint.\n \n \n \n \n\n\n \n Śmieja, M.; and Geiger, B. C\n\n\n \n\n\n\n Information Sciences, 421: 254–271. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Semi-supervisedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2017semi,\n  title={Semi-supervised cross-entropy clustering with information bottleneck constraint},\n  author={{\\'S}mieja, Marek and Geiger, Bernhard C},\n  journal={Information Sciences},\n  volume={421},\n  pages={254--271},\n  year={2017},\n  url={pubs/cec-ib.pdf},\n  doi={https://doi.org/10.1016/j.ins.2017.07.016}\n}\n\n
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\n \n\n \n \n \n \n \n \n Regression SVM for incomplete data.\n \n \n \n \n\n\n \n Struski, Ł.; Śmieja, M.; Zieliński, B.; and Tabor, J.\n\n\n \n\n\n\n Schedae Informaticae, 26: 23–35. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"RegressionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{struski2017regression,\n  title={Regression SVM for incomplete data},\n  author={Struski, {\\L}ukasz and {\\'S}mieja, Marek and Zieli{\\'n}ski, Bartosz and Tabor, Jacek},\n  journal={Schedae Informaticae},\n  volume={26},\n  pages={23--35},\n  year={2017},\n  url={https://www.ejournals.eu/pliki/art/10833/pl},\n  doi={https://doi.org/10.4467/20838476SI.17.001.6807}\n}\n\n
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\n  \n 2016\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Average Information Content Maximization – A New Approach for Fingerprint Hybridization and Reduction.\n \n \n \n \n\n\n \n Śmieja, M.; and Warszycki, D.\n\n\n \n\n\n\n PLoS ONE, 11(1): e0146666. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"AveragePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2016average,\n  title={Average Information Content Maximization -- A New Approach for Fingerprint Hybridization and Reduction},\n  author={{\\'S}mieja, Marek and Warszycki, Dawid},\n  journal={PLoS ONE},\n  volume={11},\n  number={1},\n  pages={e0146666},\n  year={2016},\n  doi={https://doi.org/10.1371/journal.pone.0146666}, url={https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0146666&type=printable}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fast entropy clustering of sparse high dimensional binary data.\n \n \n \n \n\n\n \n Śmieja, M.; Nakoneczny, S.; and Tabor, J.\n\n\n \n\n\n\n In International Joint Conference on Neural Networks (IJCNN 2016), pages 2397–2404, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2016fast,\n  title={Fast entropy clustering of sparse high dimensional binary data},\n  author={{\\'S}mieja, Marek and Nakoneczny, Szymon and Tabor, Jacek},\n  booktitle={International Joint Conference on Neural Networks (IJCNN 2016)},\n  pages={2397--2404},\n  year={2016},\n  url={pubs/SEC.pdf},\n  doi={10.1109/IJCNN.2016.7727497}\n}\n\n
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\n \n\n \n \n \n \n \n \n Natural language processing methods in biological activity prediction.\n \n \n \n \n\n\n \n Nakoneczny, S.; and Śmieja, M.\n\n\n \n\n\n\n In ECML PKDD workshop on Machine Learning and Life Science (MLLS 2016), pages 25, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"NaturalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{nakoneczny2016natural,\n  title={Natural language processing methods in biological activity prediction},\n  author={Nakoneczny, Szymon and \\'Smieja, Marek},\n  booktitle={ECML PKDD workshop on Machine Learning and Life Science (MLLS 2016)},\n  pages={25},\n  year={2016},\n  url={https://issuu.com/paweksieniewicz/docs/mlls-proceedings}\n}\n\n
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\n  \n 2015\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Weighted approach to general entropy function.\n \n \n \n \n\n\n \n Śmieja, M.\n\n\n \n\n\n\n IMA Journal of Mathematical Control and Information, 32(2): 329–341. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"WeightedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2015weighted,\n  title={Weighted approach to general entropy function},\n  author={{\\'S}mieja, Marek},\n  journal={IMA Journal of Mathematical Control and Information},\n  volume={32},\n  number={2},\n  pages={329--341},\n  year={2015},\n  doi = {https://doi.org/10.1093/imamci/dnt044},\n  url = {pubs/general.pdf},\n}\n\n
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\n \n\n \n \n \n \n \n \n Entropy approximation in lossy source coding problem.\n \n \n \n \n\n\n \n Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n Entropy, 17(5): 3400–3418. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"EntropyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2015entropy,\n  title={Entropy approximation in lossy source coding problem},\n  author={{\\'S}mieja, Marek and Tabor, Jacek},\n  journal={Entropy},\n  volume={17},\n  number={5},\n  pages={3400--3418},\n  year={2015},\n  doi={https://doi.org/10.3390/e17053400},\n  url={https://www.mdpi.com/1099-4300/17/5/3400/pdf?version=1431940525}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mixture of metrics optimization for machine learning problems.\n \n \n \n \n\n\n \n Wiercioch, M.; and Śmieja, M.\n\n\n \n\n\n\n Schedae Informaticae, 24: 79–88. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"MixturePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wiercioch2015mixture,\n  title={Mixture of metrics optimization for machine learning problems},\n  author={Wiercioch, Magdalena and \\'Smieja, Marek},\n  journal={Schedae Informaticae},\n  volume={24},\n  pages={79--88},\n  year={2015},\n  url={pubs/metrics.pdf},\n  doi={https://doi.org/10.4467/20838476SI.15.008.3030}\n}\n\n
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\n \n\n \n \n \n \n \n \n Spherical Wards clustering and generalized Voronoi diagrams.\n \n \n \n \n\n\n \n Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In Data Science and Advanced Analytics (DSAA 2015), volume 36678, pages 10, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"SphericalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2015spherical,\n  title={Spherical Wards clustering and generalized Voronoi diagrams},\n  author={{\\'S}mieja, Marek and Tabor, Jacek},\n  booktitle={Data Science and Advanced Analytics (DSAA 2015)},\n  volume={36678},\n  pages={10},\n  year={2015},\n  url={pubs/sWards.pdf},\n  doi={10.1109/DSAA.2015.7344796}\n}\n\n
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\n \n\n \n \n \n \n \n \n Probability Index of Metric Correspondence as a measure of visualization reliability.\n \n \n \n \n\n\n \n Wiercioch, M.; Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In Workshop on Machine Learning and Life Science under ECML PKDD (MLLS 2015), pages 12, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"ProbabilityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wiercioch2015probability,\n  title={Probability Index of Metric Correspondence as a measure of visualization reliability},\n  author={Wiercioch, Magdalena and {\\'S}mieja, Marek and Tabor, Jacek},\n  booktitle={Workshop on Machine Learning and Life Science under ECML PKDD (MLLS 2015)},\n  pages={12},\n  year={2015},\n  url={pubs/pimc.pdf},\n  doi={http://issuu.com/paweksieniewicz/docs/mlls_2015_proceedings/3?e=6403122/31441344}\n}\n\n
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\n  \n 2014\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Rényi entropy dimension of the mixture of measures.\n \n \n \n \n\n\n \n Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In Science and Information Conference (SAI 2014), pages 685–689, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"RényiPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2014renyi,\n  title={R{\\'e}nyi entropy dimension of the mixture of measures},\n  author={\\'Smieja, Marek and Tabor, Jacek},\n  booktitle={Science and Information Conference (SAI 2014)},\n  pages={685--689},\n  year={2014},\n  url={pubs/praca3.pdf},\n  doi={10.1109/SAI.2014.6918261}\n}\n\n
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\n \n\n \n \n \n \n \n \n Asymmetric clustering index in a case study of 5-HT1A receptor ligands.\n \n \n \n \n\n\n \n Śmieja, M.; Warszycki, D.; Tabor, J.; and Bojarski, A. J\n\n\n \n\n\n\n PLoS ONE, 9(7): e102069. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"AsymmetricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2014asymmetric,\n  title={Asymmetric clustering index in a case study of 5-HT1A receptor ligands},\n  author={{\\'S}mieja, Marek and Warszycki, Dawid and Tabor, Jacek and Bojarski, Andrzej J},\n  journal={PLoS ONE},\n  volume={9},\n  number={7},\n  pages={e102069},\n  year={2014}, url={https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0102069&type=printable},\n  doi={https://doi.org/10.1371/journal.pone.0102069}\n}\n\n
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\n \n\n \n \n \n \n \n \n Subspaces clustering approach to lossy image compression.\n \n \n \n \n\n\n \n Spurek, P.; Śmieja, M.; and Misztal, K.\n\n\n \n\n\n\n In International Conference on Computer Information Systems and Industrial Management (CISIM 2014), pages 571–579, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"SubspacesPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{spurek2015subspaces,\n  title={Subspaces clustering approach to lossy image compression},\n  author={Spurek, Przemys{\\l}aw and {\\'S}mieja, Marek and Misztal, Krzysztof},\n  booktitle={International Conference on Computer Information Systems and Industrial Management (CISIM 2014)},\n  pages={571--579},\n  year={2014},\n  url={pubs/praca6.pdf},\n  doi={https://doi.org/10.1007/978-3-662-45237-0_52}\n}\n\n
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\n  \n 2013\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Image segmentation with use of cross-entropy clustering.\n \n \n \n \n\n\n \n Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n In International Conference on Computer Recognition Systems (CORES 2013), pages 403–409, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"ImagePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{smieja2013image,\n  title={Image segmentation with use of cross-entropy clustering},\n  author={{\\'S}mieja, Marek and Tabor, Jacek},\n  booktitle={International Conference on Computer Recognition Systems (CORES 2013)},\n  pages={403--409},\n  year={2013},\n  url={pubs/image.pdf},\n  doi={https://doi.org/10.1007/978-3-319-00969-8_39}\n}\n\n
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\n  \n 2012\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Entropy of the mixture of sources and entropy dimension.\n \n \n \n \n\n\n \n Śmieja, M.; and Tabor, J.\n\n\n \n\n\n\n IEEE Transactions on Information Theory, 58(5): 2719–2728. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"EntropyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{smieja2011entropy,\n  title={Entropy of the mixture of sources and entropy dimension},\n  author={\\'Smieja, Marek and Tabor, Jacek},\n  journal={IEEE Transactions on Information Theory},\n  volume={58},\n  number={5},\n  pages={2719--2728},\n  year={2012},\n  url={http://www2.im.uj.edu.pl/badania/preprinty/imuj2011/pr1103.pdf},\n  doi={10.1109/TIT.2011.2181820}\n}\n\n
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