| Sylwester Klocek, Łukasz Maziarka, Jakub Nowak, Marek Śmieja, Jacek Tabor, Maciej Wołczyk, Hypernetwork Functional Image Representation, Artificial Neural Networks and Machine Learning – Icann 2019: Workshop and Special Sessions (2019), 496-510 |
| Szymon Maszke, Marek Śmieja, Jacek Tabor, Maciej Wołczyk, Biologically-Inspired Spatial Neural Networks, (2019), 5 |
| 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 vol. Lecture Notes in Computer Science book series (LNCS, volume 11954) (2019), 419-431 |
| Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mario A.T. Figueiredo, Iterative Imputation of Missing Data using Auto-encoder Dynamics, International Conference on Neural Information Processing [ICONIP] vol. Lecture Notes in Computer Science, volume 12534 (2020), 258-269 |
| Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka, Processing of incomplete images by (graph) convolutional neural networks, International Conference On Machine Learning (workshop Track) (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] vol. Lecture Notes in Computer Science volume 12534 (2020), 220-231 |
| Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski, Estimating conditional density of missing values using deep Gaussian mixture model, International Conference On Machine Learning (workshop Track) (2020), 6 |
| 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 vol. Lecture Notes in Computer Science, volume 12533 (2020), 512-523 |
| Marek Śmieja, Maciej Kołomycki, Łukasz Struski, Mateusz Juda, Mario A.T. Figueiredo, Can auto-encoders help with filling missing data?, International Conference On Learning Represenation (workshop Track) (2020), 6 |
| Morawiecki Paweł, Przemysław Spurek, Marek Śmieja, Jacek Tabor, Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models, European Symposium on Artificial Neural Networks [ESANN], (2020), 6 |
| Klaudia Bałazy, Igor Podolak, Marek Śmieja, Jacek Tabor, Tomasz Trzciński, Maciej Wołczyk, Bartosz Wójcik, Zero Time Waste: Recycling Predictions in Early Exit Neural Networks, Advances in Neural Information Processing Systems [NeurIPS](MAIN) vol. 34 (2021), 1-13 |
| Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek, OneFlow: One-class flow for anomaly detection based on a minimal volume region, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 44/11 (2022), 8508-8519 |
| Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski, Jacek Tabor, MisConv: Convolutional Neural Networks for Missing Data, IEEE Workshop on Applications of Computer Vision [WACV], (2022), 2060-2069 |
| Bartosz Wójcik, Jacek Grela, Marek Śmieja, Krzysztof Misztal, Jacek Tabor, SLOVA: Uncertainty estimation using single label one-vs-all classifier, Applied Soft Computing Journal vol. 126 (2022), 109219 |
| Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Zięba Maciej , Patryk Wielopolski, Rafał Kurczab, Marek Śmieja, PluGeN: Multi-Label Conditional Generation From Pre-Trained Models, National Conference of the American Association for Artificial Intelligence [AAAI](MAIN) vol. 36/8 (2022), 8647-8656 |
