Loss Functions for Clustering in Multi-instance Learning. - Marek Dedic; Tomás Pevný; Lukás Bajer; Martin Holena (ITAT, 2020)

Nested Multiple Instance Learning in Modelling of HTTP network traffic. - Tomás Pevný; Marek Dedic (CoRR, 2020)

Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification. - Sergej Borisov; Marek Dedic; Martin Holena (ITAT, 2021)

Scalable Graph Size Reduction for Efficient GNN Application. - Pavel Procházka; Michal Mares; Marek Dedic (ITAT, 2022)

Which Graph Properties Affect GNN Performance for a Given Downstream Task? - Pavel Procházka; Michal Mares; Marek Dedic (ITAT, 2023)

Balancing performance and complexity with adaptive graph coarsening. - Marek Dedic; Lukás Bajer; Pavel Procházka; Martin Holena (Tiny Papers @ ICLR, 2024)

Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs. - Pavel Procházka; Marek Dedic; Lukás Bajer (CoRR, 2024)