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ORCIDTAG-ML 2023, Honolulu, HI, USA
- Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA. Proceedings of Machine Learning Research 221, PMLR 2023 - Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Preface. 1-2 - Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou
, Aldo Guzmán-Sáenz, Bastian Rieck, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane:
ICML 2023 Topological Deep Learning Challenge: Design and Results. 3-8 - Putri A. van der Linden, David W. Romero, Erik J. Bekkers:
Learned Gridification for Efficient Point Cloud Processing. 9-20 - Gabriele Cesa, Kumar Pratik, Arash Behboodi:
Equivariant Self-supervised Deep Pose Estimation for Cryo EM. 21-36 - Linara Adilova, Amr Abourayya, Jianning Li, Amin Dada, Henning Petzka, Jan Egger, Jens Kleesiek, Michael Kamp:
FAM: Relative Flatness Aware Minimization. 37-49 - Alex Gabel, Victoria Klein, Riccardo Valperga, Jeroen S. W. Lamb, Kevin Webster, Rick Quax, Efstratios Gavves:
Learning Lie Group Symmetry Transformations with Neural Networks. 50-59 - Steinar Laenen:
One-Shot Neural Network Pruning via Spectral Graph Sparsification. 60-71 - Silas Alberti, Niclas Dern, Laura Thesing, Gitta Kutyniok:
Sumformer: Universal Approximation for Efficient Transformers. 72-86 - Justin Curry, Washington Mio, Tom Needham, Osman Berat Okutan, Florian Russold:
Topologically Attributed Graphs for Shape Discrimination. 87-101 - Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad P. Kording:
Deep Networks as Paths on the Manifold of Neural Representations. 102-133 - Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang:
Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. 134-145 - Farzaneh Heidari, Perouz Taslakian, Guillaume Rabusseau:
Explaining Graph Neural Networks Using Interpretable Local Surrogates. 146-155 - Oguzhan Keskin, Alisia Maria Lupidi, Stefano Fioravanti, Lucie Charlotte Magister, Pietro Barbiero, Pietro Lio, Francesco Giannini:
Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach. 156-168 - Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Unsupervised Embedding Quality Evaluation. 169-188 - Michael Munn, Benoit Dherin, Javier Gonzalvo:
A margin-based multiclass generalization bound via geometric complexity. 189-205 - Brian Wesley Bell, Michael Geyer, David Glickenstein, Amanda S. Fernandez, Juston Moore:
An Exact Kernel Equivalence for Finite Classification Models. 206-217 - Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers, Arnold W. M. Smeulders:
On genuine invariance learning without weight-tying. 218-227 - Yuanrong Wang, Antonio Briola, Tomaso Aste:
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity. 228-241 - Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar:
Metric Space Magnitude and Generalisation in Neural Networks. 242-253 - Frank Nielsen, Ke Sun:
Non-linear Embeddings in Hilbert Simplex Geometry. 254-266 - Jesse He, Tristan Brugère, Gal Mishne:
Product Manifold Learning with Independent Coordinate Selection. 267-277 - Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni:
Can strong structural encoding reduce the importance of Message Passing? 278-288 - Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi:
Breaking the Structure of Multilayer Perceptrons with Complex Topologies. 289-301 - Jens Agerberg, Wojciech Chachólski, Ryan Ramanujam:
Global and Relative Topological Features from Homological Invariants of Subsampled Datasets. 302-312 - Cheng Xin, Soham Mukherjee, Shreyas N. Samaga, Tamal K. Dey:
GRIL: A $2$-parameter Persistence Based Vectorization for Machine Learning. 313-333 - Clément Bonet, Laetitia Chapel, Lucas Drumetz, Nicolas Courty:
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections. 334-370 - Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann:
Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. 371-383 - Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa:
Geometrically Regularized Wasserstein Dictionary Learning. 384-403 - Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang:
The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs. 404-425 - Ilyes Batatia:
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds. 426-436 - Khalil Mathieu Hannouch, Stephan K. Chalup:
Learning To See Topological Properties In 4D Using Convolutional Neural Networks. 437-454 - Yajing Liu, Christina M. Cole, Chris Peterson, Michael Kirby:
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homology. 455-468 - Gergely Bérczi, Honglu Fan, Mingcong Zeng:
An ML approach to resolution of singularities. 469-487 - Frank Nielsen:
Fisher-Rao and pullback Hilbert cone distances on the multivariate Gaussian manifold with applications to simplification and quantization of mixtures. 488-504 - Yonghyeon Lee, Frank C. Park:
On Explicit Curvature Regularization in Deep Generative Models. 505-518 - Anant Khandelwal:
MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning. 519-533 - Antonio Briola, Tomaso Aste:
Topological Feature Selection. 534-556 - Liangchen Liu, Juncai He, Yen-Hsi Tsai:
Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions. 557-576
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