32 citations · 102 across the 39 of their papers we have counts for
6 papers · 2 filters
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…
SONG: Self-Organizing Neural Graphs
Łukasz Struski, Tomasz Danel, Marek Śmieja +2
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools. There are at least three advant…
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy +4
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by att…