32 citations · 68 across the 18 of their papers we have counts for
30 papers · 1 filter
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…
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…
Relative Molecule Self-Attention Transformer
Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel +5
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learni…
Flow-based SVDD for anomaly detection
Marcin Sendera, Marek Śmieja, Łukasz Maziarka +3
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other appro…
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…