Understanding microbiome dynamics via interpretable graph representation learning. - Kateryna Melnyk; Kuba Weimann; Tim O. F. Conrad (CoRR, 2022)

Forget Embedding Layers: Representation Learning for Cold-start in Recommender Systems. - Kuba Weimann; Tim O. F. Conrad (CoRR, 2022)

Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks. - Kuba Weimann; Tim Conrad 001 (CinC, 2023)

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification. - Kuba Weimann; Tim O. F. Conrad (IEEE J. Biomed. Health Informatics, 2024)

Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance. - Kuba Weimann; Tim O. F. Conrad (Comput. Biol. Medicine, 2025)

FELRec: efficient handling of item cold-start with dynamic representation learning in recommender systems. - Kuba Weimann; Tim O. F. Conrad (Int. J. Data Sci. Anal., 2025)