Search Results for author: Łukasz Maziarka

Found 16 papers, 7 papers with code

tsGT: Stochastic Time Series Modeling With Transformer

no code implementations8 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.

Time Series

Relative Molecule Self-Attention Transformer

no code implementations12 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.

Drug Discovery Property Prediction +1

On the relationship between disentanglement and multi-task learning

no code implementations7 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.

Disentanglement Multi-Task Learning

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

1 code implementation18 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.

Attribute Text Generation

Flow-based SVDD for anomaly detection

no code implementations10 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.

Anomaly Detection One-class classifier

Processing of incomplete images by (graph) convolutional neural networks

no code implementations26 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.


Molecule Attention Transformer

6 code implementations19 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.

Drug Discovery Property Prediction

Spatial Graph Convolutional Networks

2 code implementations11 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.

Image Classification

Non-linear ICA based on Cramer-Wold metric

no code implementations1 Mar 2019 Przemysław Spurek, Aleksandra Nowak, Jacek Tabor, Łukasz Maziarka, Stanisław Jastrzębski

Non-linear source separation is a challenging open problem with many applications.

Hypernetwork functional image representation

no code implementations27 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.

Image Super-Resolution

LOSSGRAD: automatic learning rate in gradient descent

1 code implementation20 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.

Mol-CycleGAN - a generative model for molecular optimization

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.

Set Aggregation Network as a Trainable Pooling Layer

1 code implementation3 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.

Deep processing of structured data

no code implementations27 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).

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