no code implementations • 8 Mar 2024 • Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski, Łukasz Maziarka, Marta Emilia Nowakowska, Łukasz Kaiser, Piotr Miłoś
Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data.
no code implementations • 12 Oct 2021 • Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gaiński, Jacek Tabor, Igor Podolak, Paweł Morkisz, Stanisław Jastrzębski
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.
no code implementations • 7 Oct 2021 • Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk, Andrzej Bedychaj
One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks.
1 code implementation • 18 Sep 2021 • Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja
Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.
no code implementations • 10 Aug 2021 • Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.
1 code implementation • 23 Nov 2020 • Agnieszka Pocha, Tomasz Danel, Łukasz Maziarka
Graph neural networks have recently become a standard method for analysing chemical compounds.
no code implementations • 26 Oct 2020 • Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka
We investigate the problem of training neural networks from incomplete images without replacing missing values.
no code implementations • 6 Oct 2020 • Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek
We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region.
6 code implementations • 19 Feb 2020 • Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrzębski
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 may unlock a widespread use of deep learning in the drug discovery industry.
2 code implementations • 11 Sep 2019 • Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
no code implementations • 1 Mar 2019 • Przemysław Spurek, Aleksandra Nowak, Jacek Tabor, Łukasz Maziarka, Stanisław Jastrzębski
Non-linear source separation is a challenging open problem with many applications.
no code implementations • 27 Feb 2019 • Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, Marek Śmieja
Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network.
1 code implementation • 20 Feb 2019 • Bartosz Wójcik, Łukasz Maziarka, Jacek Tabor
In this paper, we propose a simple, fast and easy to implement algorithm LOSSGRAD (locally optimal step-size in gradient descent), which automatically modifies the step-size in gradient descent during neural networks training.
1 code implementation • ICLR 2019 • Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Michał Warchoł
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties.
1 code implementation • 3 Oct 2018 • Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.
no code implementations • 27 Sep 2018 • Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek
We construct a general unified framework for learning representation of structured data, i. e. data which cannot be represented as the fixed-length vectors (e. g. sets, graphs, texts or images of varying sizes).