TAG-ML

Keyphrases

Publications

2023

Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad Kording
Deep Networks as Paths on the Manifold of Neural Representations. TAG-ML (2023)
Linara Adilova, Amr Abourayya, Jianning Li, Amin Dada, Henning Petzka, Jan Egger, Jens Kleesiek, Michael Kamp
FAM: Relative Flatness Aware Minimization. TAG-ML (2023)
Justin Curry, Washington Mio, Tom Needham, Osman Berat Okutan, Florian Russold
Topologically Attributed Graphs for Shape Discrimination. TAG-ML (2023)
Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers, Arnold W. M. Smeulders
On genuine invariance learning without weight-tying. TAG-ML (2023)
Liangchen Liu, Juncai He, Yen-Hsi Tsai
Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions. TAG-ML (2023)
Anant Khandelwal
MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning. TAG-ML (2023)
Jens Agerberg, Wojciech Chachólski, Ryan Ramanujam
Global and Relative Topological Features from Homological Invariants of Subsampled Datasets. TAG-ML (2023)
Yajing Liu, Christina M. Cole, Chris Peterson, Michael Kirby
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homology. TAG-ML (2023)
Frank Nielsen
Fisher-Rao and pullback Hilbert cone distances on the multivariate Gaussian manifold with applications to simplification and quantization of mixtures. TAG-ML (2023)
Jesse He, Tristan Brugère, Gal Mishne
Product Manifold Learning with Independent Coordinate Selection. TAG-ML (2023)
Antonio Briola, Tomaso Aste
Topological Feature Selection. TAG-ML (2023)
Gabriele Cesa, Kumar Pratik, Arash Behboodi
Equivariant Self-supervised Deep Pose Estimation for Cryo EM. TAG-ML (2023)
Michael Munn, Benoit Dherin, Javier Gonzalvo
A margin-based multiclass generalization bound via geometric complexity. TAG-ML (2023)
Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn
Preface. TAG-ML (2023)
Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang
The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs. TAG-ML (2023)
Brian Wesley Bell, Michael Geyer, David Glickenstein, Amanda S. Fernandez, Juston Moore
An Exact Kernel Equivalence for Finite Classification Models. TAG-ML (2023)
Gergely Bérczi, Honglu Fan, Mingcong Zeng
An ML approach to resolution of singularities. TAG-ML (2023)
Farzaneh Heidari, Perouz Taslakian, Guillaume Rabusseau
Explaining Graph Neural Networks Using Interpretable Local Surrogates. TAG-ML (2023)
Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa
Geometrically Regularized Wasserstein Dictionary Learning. TAG-ML (2023)
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. TAG-ML (2023)
Putri A. van der Linden, David W. Romero, Erik J. Bekkers
Learned Gridification for Efficient Point Cloud Processing. TAG-ML (2023)
Yuanrong Wang, Antonio Briola, Tomaso Aste
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity. TAG-ML (2023)
Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi
Breaking the Structure of Multilayer Perceptrons with Complex Topologies. TAG-ML (2023)
Cheng Xin, Soham Mukherjee, Shreyas N. Samaga, Tamal K. Dey
GRIL: A $2$-parameter Persistence Based Vectorization for Machine Learning. TAG-ML (2023)
Steinar Laenen
One-Shot Neural Network Pruning via Spectral Graph Sparsification. TAG-ML (2023)
Frank Nielsen, Ke Sun
Non-linear Embeddings in Hilbert Simplex Geometry. TAG-ML (2023)
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. TAG-ML (2023)
Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang
Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML (2023)
Arjun Karuvally, Peter DelMastro, Hava T. Siegelmann
Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. TAG-ML (2023)
Ilyes Batatia
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds. TAG-ML (2023)
Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni
Can strong structural encoding reduce the importance of Message Passing? TAG-ML (2023)
Clément Bonet, Laetitia Chapel, Lucas Drumetz, Nicolas Courty
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections. TAG-ML (2023)
Khalil Mathieu Hannouch, Stephan K. Chalup
Learning To See Topological Properties In 4D Using Convolutional Neural Networks. TAG-ML (2023)
Yonghyeon Lee, Frank C. Park
On Explicit Curvature Regularization in Deep Generative Models. TAG-ML (2023)
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Unsupervised Embedding Quality Evaluation. TAG-ML (2023)
Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar
Metric Space Magnitude and Generalisation in Neural Networks. TAG-ML (2023)
Silas Alberti, Niclas Dern, Laura Thesing, Gitta Kutyniok
Sumformer: Universal Approximation for Efficient Transformers. TAG-ML (2023)
Alex Gabel, Victoria Klein, Riccardo Valperga, Jeroen S. W. Lamb, Kevin Webster, Rick Quax, Efstratios Gavves
Learning Lie Group Symmetry Transformations with Neural Networks. TAG-ML (2023)

volume 221, 2023

Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA TAG-ML 221 (2023)

2022

volume 196, 2022

Topological, Algebraic and Geometric Learning Workshops 2022, 25-22 July 2022, Virtual TAG-ML 196 (2022)