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ORCID1st GRaM 2024, Vienna, Austria
- Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka:
Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 29 July 2024, Vienna, Austria. Proceedings of Machine Learning Research 251, PMLR 2024 - Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka:
Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop. 1-6 - Artem Moskalev, Mangal Prakash, Rui Liao, Tommaso Mansi:
SE(3)-Hyena Operator for Scalable Equivariant Learning. 7-19 - Yun Young Choi, Sun Woo Park, Minho Lee, Youngho Woo:
Topology-Informed Graph Transformer. 20-34 - Bao Nguyen, Anjana Yodaiken, Petar Velickovic:
Alignment of MPNNs and Graph Transformers. 35-49 - Soutrik Roy Chowdhury:
Stability Analysis of Equivariant Convolutional Representations Through The Lens of Equivariant Multi-layered CKNs. 50-64 - Pablo Monteagudo-Lago, Arielle Rosinski, Andrew Joseph Dudzik, Petar Velickovic:
Asynchrony Invariance Loss Functions for Graph Neural Networks. 65-77 - Martin Lindström, Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund:
A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry. 78-91 - Michael Schopf-Kuester, Zorah Lähner, Michael Moeller:
3D Shape Completion with Test-Time Training. 92-102 - Igor Sterner, Shiye Su, Petar Velickovic:
Commute-Time-Optimised Graphs for GNNs. 103-112 - Alonso Urbano, David W. Romero:
Self-supervised detection of perfect and partial input-dependent symmetries. 113-131 - Riccardo Ali, Paulina Kulyte, Haitz Sáez de Ocáriz Borde, Pietro Lio:
Metric Learning for Clifford Group Equivariant Neural Networks. 132-145 - Christian Nauck, Rohan Gorantla, Michael Lindner, Konstantin Schürholt, Antonia S. J. S. Mey, Frank Hellmann:
Dirac–Bianconi Graph Neural Networks – Enabling Non-Diffusive Long-Range Graph Predictions. 146-157 - Eun Som Jeon, Rahul Khurana, Aishani Pathak, Pavan Turaga:
Leveraging Topological Guidance for Improved Knowledge Distillation. 158-172 - Veljko Kovac, Erik J. Bekkers, Pietro Lió, Floor Eijkelboom:
E(n) Equivariant Message Passing Cellular Networks. 173-186 - Veronica Lachi, Francesco Ferrini, Antonio Longa, Bruno Lepri, Andrea Passerini:
A Simple and Expressive Graph Neural Network Based Method for Structural Link Representation. 187-201 - Sylvia Howland, Keerti Sahithi Kappagantula, Henry Kvinge, Tegan Emerson:
Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss. 202-211 - Audum Meyers, Timothy Doster, Colin C. Olson, Tegan Emerson:
Topological and Dynamical Representations for Radio Frequency Signal Classification. 212-221 - Diogo Lavado, Cláudia Soares, Alessandra Micheletti:
SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors. 222-232 - Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal:
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. 233-248 - Ferran Hernandez Caralt, Guillermo Bernárdez Gil, Iulia Duta, Pietro Liò, Eduard Alarcón-Cot:
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks. 249-263 - Olga Zaghen, Antonio Longa, Steve Azzolin, Lev Telyatnikov, Andrea Passerini, Pietro Liò:
Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians. 264-276 - Stas Syrota, Pablo Moreno-Muñoz, Søren Hauberg:
Decoder ensembling for learned latent geometries. 277-285 - Oline Ranum, David R. Wessels, Gomer Otterspeer, Erik J. Bekkers, Floris Roelofsen, Jari I. Andersen:
The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition. 286-302 - Lucas Roos, Steve Kroon:
On Fairly Comparing Group Equivariant Networks. 303-317 - Yingying Wu, Roger Fu, Richard Peng, Qifeng Chen:
Graph Convolutional Networks for Learning Laplace-Beltrami Operators. 318-331 - Kotaro Sakamoto, Ryosuke Sakamoto, Masato Tanabe, Masatomo Akagawa, Yusuke Hayashi, Manato Yaguchi, Masahiro Suzuki, Yutaka Matsuo:
The Geometry of Diffusion Models: Tubular Neighbourhoods and Singularities. 332-363 - Abihith Kothapalli
, Ashkan Shahbazi, Xinran Liu, Robert Sheng, Soheil Kolouri:
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification. 364-380 - Athina Sotiropoulou, David Alvarez-Melis:
Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces. 381-393 - Berfin Inal, Gabriele Cesa:
Adaptive Sampling for Continuous Group Equivariant Neural Networks. 394-419 - 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 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:
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. 420-428
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