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Generative flow-based models in application to uncertainty modeling for machine learning tasks

2021/43/B/ST6/02853

Keywords:

generative flow-based models probabilistic regression uncertainty modeling

Descriptors:

  • ST6_011:

Panel:

ST6 - Computer science and informatics: informatics and information systems, computer science, scientific computing, intelligent systems

Host institution :

Politechnika Wrocławska

woj. dolnośląskie

Other projects carried out by the institution 

Principal investigator (from the host institution):

dr hab. Maciej Mateusz Zięba 

Number of co-investigators in the project: 3

Call: OPUS 22 - announced on 2021-09-15

Amount awarded: 872 800 PLN

Project start date (Y-m-d): 2022-08-01

Project end date (Y-m-d): 2026-07-31

Project duration:: 48 months (the same as in the proposal)

Project status: Pending project

Project description

Download the project description in a pdf file

Note - project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.

Information in the final report

  • Articles in post-conference publications (4)
  1. Treeflow: going beyond tree-based parametric probabilistic regression
    Authors:
    Patryk E. Wielopolski, Maciej M. Zięba
    Conference:
    Frontiers in Artificial Intelligence and Applications (rok: 2023, tom: ECAI 2023 : 26th European Conference on Artificial Intelligence, strony: 2631-2638), Wydawca: IOS Press
    Data:
    konferencja 30.09–4.10.2023
    Status:
    Published
    DOI:
    10.3233/FAIA230570 - link to the publication
  2. Modeling uncertainty in personalized emotion prediction with normalizing flows
    Authors:
    Piotr D. Miłkowski, Konrad Karanowski, Patryk E. Wielopolski, Jan Kocoń, Przemysław Kazienko, Maciej M. Zięba
    Conference:
    2023 IEEE International Conference on Data Mining Workshops (ICDMW) (rok: 2023, tom: 23nd IEEE International Conference on Data Mining Workshops (ICDMW),, strony: 757-766), Wydawca: Institute of Electrical and Electronics Engineers
    Data:
    konferencja 1.12.2023-4.12.2023
    Status:
    Published
    DOI:
    10.1109/ICDMW60847.2023.00103 - link to the publication
  3. FlowHMM: fow-based continuous hidden Markov models
    Authors:
    Paweł Lorek, Rafał Nowak, Tomasz Trzciński, Maciej M. Zięba
    Conference:
    Advances in Neural Information Processing Systems. ISSN 1049-5258; 35 (rok: 2022, tom: 35th Conference on Neural Information Processing Systems, NeurIPS 2022, strony: 45674), Wydawca: Neural Information Processing Systems Foundation
    Data:
    konferencja 28.11-9.12.2022
    Status:
    Published
    DOI:
    link to the publication
  4. Classical out-of-distribution detection methods benchmark in text classification tasks
    Authors:
    Mateusz Baran, Joanna Baran, Mateusz A. Wójcik, Maciej M. Zięba, Adam F. Gonczarek
    Conference:
    Proceedings of the Student Research Workshop (rok: 2023, tom: The 61st Annual Meeting of the Association for Computational Linguistics, ACL-SRW 2023, strony: 119-129), Wydawca: Association for Computational Linguistics
    Data:
    konferencja 10-12.07.2023
    Status:
    Published
    DOI:
    10.18653/v1/2023.acl-srw.20 - link to the publication

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