4 citations · 10 across the 6 of their papers we have counts for
7 papers
SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
Kacper Jurek, Wojciech Batko, Marek Śmieja +1
Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular…
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…
Hypernetwork approach to Bayesian MAML
Piotr Borycki, Piotr Kubacki, Marcin Przewięźlikowski +3
The main goal of Few-Shot learning algorithms is to enable learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic M…
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…
MisConv: Convolutional Neural Networks for Missing Data
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski +1
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image…
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…