3 citations · 3 across the 4 of their papers we have counts for
11 papers
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…
On the relationship between disentanglement and multi-task learning
Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk +1
One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks. At the same time finding a joint, adapta…
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…
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction
Agnieszka Pocha, Tomasz Danel, Łukasz Maziarka
Graph neural networks have recently become a standard method for analysing chemical compounds. In the field of molecular property prediction, the emphasis is now put on designing n…
Processing of incomplete images by (graph) convolutional neural networks
Tomasz Danel, Marek Śmieja, Łukasz Struski +2
We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which m…
Molecule Attention Transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha +3
Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution…