activity
20182022
most citedNon-Gaussian Gaussian Processes for Few-Shot Regression

5 citations · 17 across the 8 of their papers we have counts for

collaborators

15 papers

cs.CV2022

Two-headed eye-segmentation approach for biometric identification

Wiktor Lazarski, Maciej Zieba, Tanguy Jeanneau +2

Iris-based identification systems are among the most popular approaches for person identification. Such systems require good-quality segmentation modules that ideally identify the…

cs.LG2022

Continual learning on 3D point clouds with random compressed rehearsal

Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski +2

Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype…

cs.LG20223 cited

HyperShot: Few-Shot Learning by Kernel HyperNetworks

Marcin Sendera, Marcin Przewięźlikowski, Konrad Karanowski +3

Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one eleme…

cs.LG20215 cited

Non-Gaussian Gaussian Processes for Few-Shot Regression

Marcin Sendera, Jacek Tabor, Aleksandra Nowak +5

Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction…

cs.CV2021

Flow Plugin Network for conditional generation

Patryk Wielopolski, Michał Koperski, Maciej Zięba

Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud gener…

cs.LG20204 cited

RegFlow: Probabilistic Flow-based Regression for Future Prediction

Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja +3

Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenar…