| 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 | 19 | 2019 |
| Biologically-Inspired Spatial Neural Networks M Wołczyk, J Tabor, M Śmieja, S Maszke arXiv preprint arXiv:1910.02776, 2019 | 2 | 2019 |
| Continual World: A Robotic Benchmark For Continual Reinforcement Learning M Wołczyk, M Zając, R Pascanu, Ł Kuciński, P Miłoś arXiv preprint arXiv:2105.10919, 2021 | 1 | 2021 |
| SeGMA: Semi-Supervised Gaussian Mixture Autoencoder M Śmieja, M Wołczyk, J Tabor, BC Geiger IEEE Transactions on Neural Networks and Learning Systems, 2020 | 1 | 2020 |
| Zero Time Waste: Recycling Predictions in Early Exit Neural Networks M Wołczyk, B Wójcik, K Bałazy, I Podolak, J Tabor, M Śmieja, T Trzciński arXiv preprint arXiv:2106.05409, 2021 | | 2021 |
| Remember More by Recalling Less: Investigating the Role of Batch Size in Continual Learning with Experience Replay (Student Abstract) M Wołczyk, A Krutsylo Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15923 …, 2021 | | 2021 |
| Finding the Optimal Network Depth in Classification Tasks B Wójcik, M Wołczyk, K Bałazy, J Tabor arXiv preprint arXiv:2004.08172, 2020 | | 2020 |
| Deep, generative autoencoders for semi-supervised learning M Wołczyk | | 2019 |
