Attention-based deep multiple instance learning M Ilse, J Tomczak, M Welling International conference on machine learning, 2127-2136, 2018 | 1727 | 2018 |
VAE with a VampPrior J Tomczak, M Welling International conference on artificial intelligence and statistics, 1214-1223, 2018 | 696 | 2018 |
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction M Zięba, SK Tomczak, JM Tomczak Expert systems with applications 58, 93-101, 2016 | 455 | 2016 |
Hyperspherical variational auto-encoders TR Davidson, L Falorsi, N De Cao, T Kipf, JM Tomczak arXiv preprint arXiv:1804.00891, 2018 | 454 | 2018 |
Sylvester Normalizing Flows for Variational Inference R Berg, L Hasenclever, JM Tomczak, M Welling arXiv preprint arXiv:1803.05649, 2018 | 245 | 2018 |
Video compression with rate-distortion autoencoders A Habibian, T Rozendaal, JM Tomczak, TS Cohen Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 224 | 2019 |
Diva: Domain invariant variational autoencoders M Ilse, JM Tomczak, C Louizos, M Welling Medical Imaging with Deep Learning, 322-348, 2020 | 211 | 2020 |
Conditional channel gated networks for task-aware continual learning D Abati, J Tomczak, T Blankevoort, S Calderara, R Cucchiara, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 205 | 2020 |
Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients M Zięba, JM Tomczak, M Lubicz, J Świątek Applied soft computing 14, 99-108, 2014 | 200 | 2014 |
Improving variational auto-encoders using householder flow JM Tomczak, M Welling arXiv preprint arXiv:1611.09630, 2016 | 170 | 2016 |
Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2 E Weglarz-Tomczak, JM Tomczak, M Talma, M Burda-Grabowska, ... Scientific reports 11 (1), 3640, 2021 | 117 | 2021 |
Ckconv: Continuous kernel convolution for sequential data DW Romero, A Kuzina, EJ Bekkers, JM Tomczak, M Hoogendoorn arXiv preprint arXiv:2102.02611, 2021 | 116 | 2021 |
Combinatorial Bayesian Optimization using the Graph Cartesian Product C Oh, J Tomczak, E Gavves, M Welling Advances in Neural Information Processing Systems, 2914-2924, 2019 | 107 | 2019 |
Classification restricted Boltzmann machine for comprehensible credit scoring model JM Tomczak, M Zięba Expert Systems with Applications 42 (4), 1789-1796, 2015 | 107 | 2015 |
Why Deep Generative Modeling? JM Tomczak Deep Generative Modeling, 1-12, 2022 | 97 | 2022 |
Attentive group equivariant convolutional networks D Romero, E Bekkers, J Tomczak, M Hoogendoorn International Conference on Machine Learning, 8188-8199, 2020 | 86 | 2020 |
Interaction prediction in structure-based virtual screening using deep learning A Gonczarek, JM Tomczak, S Zaręba, J Kaczmar, P Dąbrowski, ... Computers in biology and medicine 100, 253-258, 2018 | 85 | 2018 |
Flexconv: Continuous kernel convolutions with differentiable kernel sizes DW Romero, RJ Bruintjes, JM Tomczak, EJ Bekkers, M Hoogendoorn, ... arXiv preprint arXiv:2110.08059, 2021 | 83 | 2021 |
Time efficiency in optimization with a bayesian-evolutionary algorithm G Lan, JM Tomczak, DM Roijers, AE Eiben Swarm and Evolutionary Computation 69, 100970, 2022 | 60 | 2022 |
Selecting data augmentation for simulating interventions M Ilse, JM Tomczak, P Forré International conference on machine learning, 4555-4562, 2021 | 59 | 2021 |