Hypershot: Few-shot learning by kernel hypernetworks M Sendera, M Przewięźlikowski, K Karanowski, M Zięba, J Tabor, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 21 | 2023 |
Hypermaml: Few-shot adaptation of deep models with hypernetworks M Przewięźlikowski, P Przybysz, J Tabor, M Zięba, P Spurek Neurocomputing 598, 128179, 2024 | 13 | 2024 |
Estimating conditional density of missing values using deep Gaussian mixture model M Przewięźlikowski, M Śmieja, Ł Struski International Conference on Neural Information Processing 2020, 220-231, 2020 | 11 | 2020 |
Regflow: Probabilistic flow-based regression for future prediction M Zięba, M Przewięźlikowski, M Śmieja, J Tabor, T Trzciński, P Spurek Asian Conference on Intelligent Information and Database Systems, 267-279, 2024 | 7 | 2024 |
MisConv: Convolutional Neural Networks for Missing Data M Przewięźlikowski, M Śmieja, Ł Struski, J Tabor Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 4 | 2022 |
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning F Szatkowski, M Pyla, M Przewięźlikowski, S Cygert, B Twardowski, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 3 | 2024 |
Zero time waste in pre-trained early exit neural networks B Wójcik, M Przewiȩźlikowski, F Szatkowski, M Wołczyk, K Bałazy, ... Neural Networks 168, 580-601, 2023 | 3 | 2023 |
The general framework for few-shot learning by kernel HyperNetworks JTPS Marcin Sendera, Marcin Przewiȩźlikowski, Jan Miksa, Mateusz Rajski ... Machine Vision and Applications 34, 2023 | 3 | 2023 |
Hypernetwork approach to Bayesian MAML P Borycki, P Kubacki, M Przewięźlikowski, T Kuśmierczyk, J Tabor, ... arXiv preprint arXiv:2210.02796, 2022 | 1 | 2022 |
A deep cut into Split Federated Self-supervised Learning M Przewięźlikowski, M Osial, B Zieliński, M Śmieja Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024 | | 2024 |
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation P Batorski, D Malarz, M Przewięźlikowski, M Mazur, S Tadeja, P Spurek arXiv preprint arXiv:2402.01524, 2024 | | 2024 |
Augmentation-aware Self-supervised Learning with Conditioned Projector M Przewięźlikowski, M Pyla, B Zieliński, B Twardowski, J Tabor, M Śmieja arXiv preprint arXiv:2306.06082, 2023 | | 2023 |
Przetwarzanie niepełnych danych za pomocą konwolucyjnych sieci neuronowych M Przewięźlikowski | | 2022 |
Support for high-level quantum Bayesian inference M Przewięźlikowski, M Grabowski, D Kurzyk, K Rycerz Computational Science–ICCS 2019: 19th International Conference, Faro …, 2019 | | 2019 |
HyperShot: Few-Shot Learning by Kernel HyperNetworks–supplementary material M Sendera, M Przewiezlikowski, K Karanowski, M Zieba, J Tabor, ... | | |
Using Bayes Nets for Quantum Inferring of Acausal Systems of Events M Przewięźlikowski, M Grabowski, K Rycerz, D Kurzyk, P Gawron | | |