Group of Machine Learning Research

GMUM (Group of Machine Learning Research) is a group at Jagiellonian University working on various aspects of machine learning, and in particular deep learning - in both fundamental and applied setting. The group is led by prof. Jacek Tabor.
Some of the research directions our group pursues include:

generative models: efficient training and sampling; inpainting; super-resolution,
theoretical understanding of deep learning and optimization,
natural language processing,
drug design and cheminformatics,
unsupervised learning and clustering,
computer vision and medical image analysis.

MEiN scholarship for outstanding young scientists 2021 (scholarship)

Tomasz Trzciński

MEiN scholarship for outstanding young scientists 2021 (scholarship)

Marek Śmieja

START 2021 (scholarship)

Stanisław Jastrzębski

ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification (paper)

Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński

SIGKDD 2021

Robust Learning-Augmented Caching: An Experimental Study (paper)

Jakub Chłędowski, Adam Polak, Bartosz Szabucki, Konrad Żołna

ICML 2021

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization (paper)

Stanisław Jastrzębski, Devansh Arpit, Oliver Astrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho*, Krzysztof Geras*

ICML 2021

Explaining Self-Supervised Image Representations with Visual Probing (paper)

Dominika Basaj, Witold Oleszkiewicz, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Tomasz Trzciński, Bartosz Zieliński

IJCAI 2021

Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning (paper)

Dawid Rymarczyk, Adriana Borowa, Jacek Tabor, Bartosz Zieliński

WACV 2021

Group of Machine Learning Research

Recent achievements

MEiN scholarship for outstanding young scientists 2021 (scholarship)

MEiN scholarship for outstanding young scientists 2021 (scholarship)

START 2021 (scholarship)

ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification (paper)

Robust Learning-Augmented Caching: An Experimental Study (paper)

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization (paper)

Explaining Self-Supervised Image Representations with Visual Probing (paper)

Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning (paper)

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