Interpretable ECG classification via a query-based latent space traversal (qLST). - Melle B. Vessies; Sharvaree P. Vadgama; Rutger R. van de Leur; Pieter A. F. M. Doevendans; Rutger J. Hassink; Erik J. Bekkers; René van Es (CoRR, 2021)

Continuous Kendall Shape Variational Autoencoders. - Sharvaree P. Vadgama; Jakub M. Tomczak; Erik J. Bekkers (GSI (1), 2023)

Geometric Contrastive Learning. - Yeskendir Koishekenov; Sharvaree P. Vadgama; Riccardo Valperga; Erik J. Bekkers (ICCV (Workshops), 2023)

Modeling Barrett's Esophagus Progression Using Geometric Variational Autoencoders. - Vivien van Veldhuizen; Sharvaree P. Vadgama; Onno J. de Boer; Sybren L. Meijer; Erik J. Bekkers (CaPTion@MICCAI, 2023)

Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space. - Erik J. Bekkers; Sharvaree P. Vadgama; Rob Hesselink; Putri A. van der Linden; David W. Romero (ICLR, 2024)

Learning symmetries via weight-sharing with doubly stochastic tensors. - Putri A. van der Linden; Alejandro García-Castellanos; Sharvaree P. Vadgama; Thijs P. Kuipers; Erik J. Bekkers (NeurIPS, 2024)

The Hidden Pitfalls of the Cosine Similarity Loss. - Andrew Draganov; Sharvaree P. Vadgama; Erik J. Bekkers (CoRR, 2024)

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. - Guillermo Bernárdez; Lev Telyatnikov; Marco Montagna; Federica Baccini; Mathilde Papillon; Miquel Ferriol Galmés; Mustafa Hajij; Theodore Papamarkou; Maria Sofia Bucarelli; Olga Zaghen; Johan Mathe; Audun Myers; et al. (CoRR, 2024)

Grounding Continuous Representations in Geometry: Equivariant Neural Fields. - David R. Wessels; David M. Knigge; Riccardo Valperga; Samuele Papa; Sharvaree P. Vadgama; Efstratios Gavves; Erik J. Bekkers (ICLR, 2025)

On the Utility of Equivariance and Symmetry Breaking in Deep Learning Architectures on Point Clouds. - Sharvaree P. Vadgama; Mohammad Mohaiminul Islam; Domas Buracus; Christian Shewmake; Erik J. Bekkers (CoRR, 2025)

On the Importance of Embedding Norms in Self-Supervised Learning. - Andrew Draganov; Sharvaree P. Vadgama; Sebastian Damrich; Jan Niklas Böhm; Lucas Maes; Dmitry Kobak; Erik J. Bekkers (CoRR, 2025)