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Department of Artificial Intelligence
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Publications

Publications assigned to this group and public publications confirmed in the research records of its current members.

Publications: 356

  1. Uncertainty-weighted spectral similarity for hyperspectral imaging spectroscopy

    Katarzyna Jabłońska, Marcin P Jakubowicz, Dariusz Tanajewski

    IEEE Geoscience and Remote Sensing Letters · 2026

  2. Learning representations from 3D Gaussian splats

    Julia Farganus, Krzysztof Żurawicki, Weronika Z Jakubowska, Halina Kwaśnicka, Arkadiusz Gaweł

    ICCCI 2026 · 2027

  3. Unifying deep stochastic processes for image enhancement

    Wojciech M. Kozłowski, Radosław Kuczbański, Kamil Adamczewski, Karol M Szczypkowski, Maciej M Zięba

    Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea, 6th-11th July 2026 · 2026

  4. Attention sinks as internal signals for hallucination detection in large language models

    Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz

    Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea, 6th-11th July 2026 · 2026

  5. DART: a degradation-aware recurrent transformer for archival film restoration

    Mikołaj Jastrzębski, Wojciech M Kozłowski, Kamil Adamczewski

    Raporty Katedry Sztucznej Inteligencji Politechniki Wrocławskiej. 2026, Ser. PRE · 2026

  6. UnHype: CLIP-guided hypernetworks for dynamic LoRA unlearning

    Wojciech M Gromski, Maciej M Zięba, Piotr Wójcik, Maksym Petrenko, Przemysław Spurek

    Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea, 6th-11th July 2026 · 2026

  7. AbsoluteDegradation: a physics-inspired synthetic film-degradation pipeline and archival film restoration benchmark

    Mikołaj Jastrzębski, Dawid Glinkowski, Dawid Zieliński, Daniel Borkowski, Wojciech M Kozłowski, Kamil Adamczewski

    Raporty Katedry Sztucznej Inteligencji Politechniki Wrocławskiej. 2026, Ser. PRE · 2026

  8. Unifying perspectives: plausible counterfactual explanations on global, group-wise, and local levels

    Oleksii Furman, Patryk E Wielopolski, Łukasz D Lenkiewicz, Maciej M Zięba, Jerzy Stefanowski

    Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2026, Bremen, 15-21 August · 2026

  9. Towards plausibility in time series counterfactual explanations

    Marcin R. Kostrzewa, Krzysztof J Galus, Maciej M Zięba

    ACIIDS 2026 · 2026

  10. Reducing estimation uncertainty using normalizing flows and stratification

    Maciej M Zięba, Paweł Lorek, Rafał Nowak, Rafał Topolnicki, Tomasz Trzciński, Aleksandra Krystecka

    ACIIDS 2026 · 2026

  11. Plausible visual counterfactual explanations in latent space with normalizing flows

    Łukasz D. Lenkiewicz, Marcel Musiałek, Oleksii Furman, Maciej M Zięba

    ICCS 2026 · 2026

  12. HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data

    Oleksii Furman, Patryk Marszałek, Kamil Książek, Ulvi Movsum-zada, Przemysław Spurek, Marek Śmieja

    Neurocomputing · 2026

  13. Absolute cinema - old movies restoration using spatio-temporal restoration network

    Daniel Borkowski, Dawid Glinkowski, Mikołaj Jastrzębski, Dawid Zieliński, Wojciech M Kozłowski

    PP-RAI 2026 · 2026

  14. Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods

    Mariusz Pelc, Kamil Adamczewski, Katarzyna M Białas, Rafał Chałupnik, Dawid Krutul, Dawid T Wolkiewicz, Aleksandra D Kawala-Sterniuk, Magda Żołubak, Dariusz Mikołajewski, Adrian Luckiewicz, Mateusz Koryciński, Waldemar Karwowski

    Applied Sciences · 2026

  15. Metric learning approach to mineral mapping using multi-sensor satellite data

    Katarzyna Jabłońska, Maciej M Zięba

    ICCS 2026 · 2026

  16. Unboxing the black box: a survey on mechanistic interpretability for algorithmic understanding of neural networks

    Bianka S. Kowalska, Halina Kwaśnicka

    Machine Learning · 2026

  17. From networks to circuits: a structured approach to AI interpretability

    Bianka S. Kowalska, Halina Kwaśnicka

    ICCCI 2025 · 2026

  18. GaINeR: geometry-aware implicit neural representation for image editing

    Weronika Z. Jakubowska, Maciej M Zięba, Mikołaj Zieliński, Rafał Tobiasz, Krzysztof Byrski, Dominik Belter, Przemysław Spurek

    ECCV 2026 · 2026

  19. Mind the gap: quantifying the domain gap in cross-sensor diffusion super-resolution

    Dawid A. Kopeć, Katarzyna Jabłońska, Wojciech M Kozłowski, Maciej M Zięba

    ICCS 2026 · 2026

  20. Metric learning networks for accurate mineral detection with different augmentation techniques

    Katarzyna Jabłońska, Maciej M Zięba, Dariusz Tanajewski

    Expert Systems with Applications · 2026