Sharvaree P. Vadgama

According to our database, Sharvaree P. Vadgama authored at least 11 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, February, 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, January, 2025

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, Scott Mahan, Hansen Lillemark, Sharvaree P. Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane

CoRR, 2024

The Hidden Pitfalls of the Cosine Similarity Loss.

Andrew Draganov, Sharvaree P. Vadgama, Erik J. Bekkers

CoRR, 2024

Grounding Continuous Representations in Geometry: Equivariant Neural Fields.

David R. Wessels, David M. Knigge, Samuele Papa, Riccardo Valperga, Sharvaree P. Vadgama, Efstratios Gavves, Erik J. Bekkers

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Proceedings of the Cancer Prevention Through Early Detection, 2023

Geometric Contrastive Learning.

Yeskendir Koishekenov, Sharvaree P. Vadgama, Riccardo Valperga, Erik J. Bekkers

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Continuous Kendall Shape Variational Autoencoders.

Sharvaree P. Vadgama, Jakub M. Tomczak, Erik J. Bekkers

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2021

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