Marcin Przewiezlikowski

According to our database, Marcin Przewiezlikowski authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation.

Pawel Batorski, Dawid Malarz, Marcin Przewiezlikowski, Marcin Mazur, Slawomir Konrad Tadeja, Przemyslaw Spurek

CoRR, 2024

A Deep Cut Into Split Federated Self-Supervised Learning.

Marcin Przewiezlikowski, Marcin Osial, Bartosz Zielinski, Marek Smieja

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

2023

Zero time waste in pre-trained early exit neural networks.

Bartosz Wójcik, Marcin Przewiezlikowski, Filip Szatkowski, Maciej Wolczyk, Klaudia Balazy, Bartlomiej Krzepkowski, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski

Neural Networks, November, 2023

The general framework for few-shot learning by kernel HyperNetworks.

Marcin Sendera, Marcin Przewiezlikowski, Jan Miksa, Mateusz Rajski, Konrad Karanowski, Maciej Zieba, Jacek Tabor, Przemyslaw Spurek

Mach. Vis. Appl., July, 2023

Augmentation-aware Self-supervised Learning with Guided Projector.

Marcin Przewiezlikowski, Mateusz Pyla, Bartosz Zielinski, Bartlomiej Twardowski, Jacek Tabor, Marek Smieja

CoRR, 2023

HyperShot: Few-Shot Learning by Kernel HyperNetworks.

Marcin Sendera, Marcin Przewiezlikowski, Konrad Karanowski, Maciej Zieba, Jacek Tabor, Przemyslaw Spurek

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning.

Filip Szatkowski, Mateusz Pyla, Marcin Przewiezlikowski, Sebastian Cygert, Bartlomiej Twardowski, Tomasz Trzcinski

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Hypernetwork approach to Bayesian MAML.

Piotr Borycki, Piotr Kubacki, Marcin Przewiezlikowski, Tomasz Kusmierczyk, Jacek Tabor, Przemyslaw Spurek

CoRR, 2022

HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks.

Marcin Przewiezlikowski, P. Przybysz, Jacek Tabor, Maciej Zieba, Przemyslaw Spurek

CoRR, 2022

MisConv: Convolutional Neural Networks for Missing Data.

Marcin Przewiezlikowski, Marek Smieja, Lukasz Struski, Jacek Tabor

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2020

RegFlow: Probabilistic Flow-based Regression for Future Prediction.

Maciej Zieba, Marcin Przewiezlikowski, Marek Smieja, Jacek Tabor, Tomasz Trzcinski, Przemyslaw Spurek

CoRR, 2020

Estimating Conditional Density of Missing Values Using Deep Gaussian Mixture Model.

Marcin Przewiezlikowski, Marek Smieja, Lukasz Struski

Proceedings of the Neural Information Processing - 27th International Conference, 2020

2019

Support for High-Level Quantum Bayesian Inference.

Marcin Przewiezlikowski, Michal Grabowski, Dariusz Kurzyk, Katarzyna Rycerz

Proceedings of the Computational Science - ICCS 2019, 2019