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Marcin Sendera
Marcin Sendera
PhD Student, Jagiellonian University, Research Intern at MILA - Quebec AI Institute,
Zweryfikowany adres z doctoral.uj.edu.pl
Tytuł
Cytowane przez
Cytowane przez
Rok
Non-gaussian gaussian processes for few-shot regression
M Sendera, J Tabor, A Nowak, A Bedychaj, M Patacchiola, T Trzcinski, ...
Advances in Neural Information Processing Systems 34, 10285-10298, 2021
122021
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
112023
Supermodeling: the next level of abstraction in the use of data assimilation
M Sendera, GS Duane, W Dzwinel
Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020
92020
Hybrid swarm and agent-based evolutionary optimization
L Placzkiewicz, M Sendera, A Szlachta, M Paciorek, A Byrski, ...
Computational Science–ICCS 2018: 18th International Conference, Wuxi, China …, 2018
82018
Data adaptation in handy economy-ideology model
M Sendera
arXiv preprint arXiv:1904.04309, 2019
52019
OneFlow: One-class flow for anomaly detection based on a minimal volume region
Ł Maziarka, M Śmieja, M Sendera, Ł Struski, J Tabor, P Spurek
IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 8508 …, 2021
22021
Flow-based anomaly detection
Ł Maziarka, M Śmieja, M Sendera, Ł Struski, J Tabor, P Spurek
arXiv preprint arXiv:2010.03002, 2020
22020
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
T Akhound-Sadegh, J Rector-Brooks, AJ Bose, S Mittal, P Lemos, CH Liu, ...
arXiv preprint arXiv:2402.06121, 2024
12024
On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
M Sendera, M Kim, S Mittal, P Lemos, L Scimeca, J Rector-Brooks, ...
arXiv preprint arXiv:2402.05098, 2024
2024
The general framework for few-shot learning by kernel HyperNetworks
M Sendera, M Przewiȩźlikowski, J Miksa, M Rajski, K Karanowski, ...
Machine Vision and Applications 34 (4), 53, 2023
2023
Missing Glow Phenomenon: Learning Disentangled Representation of Missing Data
M Sendera, Ł Struski, P Spurek
International Conference on Neural Information Processing, 196-204, 2021
2021
Flow-based SVDD for anomaly detection
M Sendera, M Śmieja, Ł Maziarka, Ł Struski, P Spurek, J Tabor
arXiv preprint arXiv:2108.04907, 2021
2021
Machine Classification of Methylomes in Cancer
I Newsham, M Sendera, SG Jammula, R Fitzgerald, C Massie, ...
bioRxiv, 2020.04. 04.025155, 2020
2020
HyperShot: Few-Shot Learning by Kernel HyperNetworks–supplementary material
M Sendera, M Przewiezlikowski, K Karanowski, M Zieba, J Tabor, ...
Supermodeling: the second level of abstraction of data assimilation procedure
M Sendera, G Duane, W Dzwinel
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