Lukasz Maziarka

According to our database, Lukasz Maziarka authored at least 25 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Relative molecule self-attention transformer.

Lukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gainski, Jacek Tabor, Igor T. Podolak, Pawel M. Morkisz, Stanislaw Jastrzebski

J. Cheminformatics, December, 2024

Multi-Label Conditional Generation From Pre-Trained Models.

Magdalena Proszewska, Maciej Wolczyk, Maciej Zieba, Patryk Wielopolski, Lukasz Maziarka, Marek Smieja

IEEE Trans. Pattern Anal. Mach. Intell., September, 2024

tsGT: Stochastic Time Series Modeling With Transformer.

Lukasz Kucinski, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski, Lukasz Maziarka, Marta Emilia Nowakowska, Lukasz Kaiser, Piotr Milos

CoRR, 2024

2023

Extended study on atomic featurization in graph neural networks for molecular property prediction.

Agnieszka Wojtuch, Tomasz Danel, Sabina Podlewska, Lukasz Maziarka

J. Cheminformatics, December, 2023

2022

OneFlow: One-Class Flow for Anomaly Detection Based on a Minimal Volume Region.

Lukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, Przemyslaw Spurek

IEEE Trans. Pattern Anal. Mach. Intell., 2022

On the Relationship Between Disentanglement and Multi-task Learning.

Lukasz Maziarka, Aleksandra Nowak, Maciej Wolczyk, Andrzej Bedychaj

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

PluGeN: Multi-Label Conditional Generation from Pre-trained Models.

Maciej Wolczyk, Magdalena Proszewska, Lukasz Maziarka, Maciej Zieba, Patryk Wielopolski, Rafal Kurczab, Marek Smieja

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract).

Piotr Gainski, Lukasz Maziarka, Tomasz Danel, Stanislaw Jastrzebski

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Flow-based SVDD for anomaly detection.

Marcin Sendera, Marek Smieja, Lukasz Maziarka, Lukasz Struski, Przemyslaw Spurek, Jacek Tabor

CoRR, 2021

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction.

Agnieszka Pocha, Tomasz Danel, Sabina Podlewska, Jacek Tabor, Lukasz Maziarka

Proceedings of the International Joint Conference on Neural Networks, 2021

Multitask Learning Using BERT with Task-Embedded Attention.

Lukasz Maziarka, Tomasz Danel

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Mol-CycleGAN: a generative model for molecular optimization.

Lukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, Michal Warchol

J. Cheminformatics, 2020

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction.

Agnieszka Pocha, Tomasz Danel, Lukasz Maziarka

CoRR, 2020

Flow-based anomaly detection.

Lukasz Maziarka, Marek Smieja, Marcin Sendera, Lukasz Struski, Jacek Tabor, Przemyslaw Spurek

CoRR, 2020

Molecule Attention Transformer.

Lukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanislaw Jastrzebski

CoRR, 2020

Non-linear ICA Based on Cramer-Wold Metric.

Przemyslaw Spurek, Aleksandra Nowak, Jacek Tabor, Lukasz Maziarka, Stanislaw Jastrzebski

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Spatial Graph Convolutional Networks.

Tomasz Danel, Przemyslaw Spurek, Jacek Tabor, Marek Smieja, Lukasz Struski, Agnieszka Slowik, Lukasz Maziarka

Proceedings of the Neural Information Processing - 27th International Conference, 2020

Processing of Incomplete Images by (Graph) Convolutional Neural Networks.

Tomasz Danel, Marek Smieja, Lukasz Struski, Przemyslaw Spurek, Lukasz Maziarka

Proceedings of the Neural Information Processing - 27th International Conference, 2020

2019

Geometric Graph Convolutional Neural Networks.

Przemyslaw Spurek, Tomasz Danel, Jacek Tabor, Marek Smieja, Lukasz Struski, Agnieszka Slowik, Lukasz Maziarka

CoRR, 2019

Multi-task hypernetworks.

Sylwester Klocek, Lukasz Maziarka, Maciej Wolczyk, Jacek Tabor, Marek Smieja, Jakub Nowak

CoRR, 2019

LOSSGRAD: automatic learning rate in gradient descent.

Bartosz Wójcik, Lukasz Maziarka, Jacek Tabor

CoRR, 2019

Set Aggregation Network as a Trainable Pooling Layer.

Lukasz Maziarka, Marek Smieja, Aleksandra Nowak, Jacek Tabor, Lukasz Struski, Przemyslaw Spurek

Proceedings of the Neural Information Processing - 26th International Conference, 2019

Mol-CycleGAN - A Generative Model for Molecular Optimization.

Lukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Michal Warchol

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

Hypernetwork Functional Image Representation.

Sylwester Klocek, Lukasz Maziarka, Maciej Wolczyk, Jacek Tabor, Jakub Nowak, Marek Smieja

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

2018

Deep processing of structured data.

Lukasz Maziarka, Marek Smieja, Aleksandra Nowak, Jacek Tabor, Lukasz Struski, Przemyslaw Spurek

CoRR, 2018