Accepted Papers
1. QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings (Poster)
Dai Quoc Nguyen, Dinh Phung
2. Quaternion Graph Neural Networks (Poster)
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
3. Learning Hyperbolic Representations for Unsupervised 3D Segmentation (Contributed Talk)
Joy Hsu, Jeffrey Gu, Serena Yeung
4. The Intrinsic Dimension of Images and Its Impact on Learning (Poster)
Phillip Pope, Chen Zhu, Micah Goldblum, Ahmed Abdelkader, Tom Goldstein
6. Universal Approximation Property of Neural Ordinary Differential Equations (Poster)
Takeshi Teshima Teshima, Koichi Tojo, Masahiro Ikeda, Isao Ishikawa, Kenta Oono
7. Affinity guided Geometric Semi-Supervised Metric Learning (Poster)
Ujjal Dutta, Mehrtash Harandi, C Chandra Shekhar
8. Sparsifying networks by traversing Geodesics (Poster)
Guruprasad Raghavan, Matt Thomson
9. Hermitian Symmetric Spaces for Graph Embeddings (Poster)
Federico Lopez, Beatrice Pozzetti, Steve Trettel, Anna Wienhard
10. Convex Optimization for Blind Source Separation on a Statistical Manifold (Poster)
Simon Luo, Lamiae Azizi, Mahito Sugiyama
11. Isometric Gaussian Process Latent Variable Model (Poster)
Martin Jørgensen, Søren Hauberg
14. Witness Autoencoder: Shaping the Latent Space with Witness Complexes (Contributed Talk)
Anastasiia Varava, Danica Kragic, Simon Schönenberger, Vladislav Polianskii , Jen Jen Chung, Vladislav Polianskii
15. A Riemannian flow perspective on learning deep linear neural networks (Contributed Talk)
Ulrich Terstiege, Holger Rauhut, Bubacarr Bah, Michael Westdickenberg
16. Unsupervised Orientation Learning Using Autoencoders (Poster)
Rembert Daems, Francis Wyffels
17. GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability (Poster)
Arinbjörn Kolbeinsson, Nicholas Jennings, Marc Deisenroth, Daniel Lengyel, Janith Petangoda, Michalis Lazarou, Kate Highnam, John Falk
18. Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization (Poster)
Navya Nagananda, Breton Minnehan, Andreas Savakis
20. Graph of Thrones : Adversarial Perturbations dismantle Aristocracy in Graphs (Poster)
Adarsh Jamadandi, Uma Mudenagudi
21. Towards Geometric Understanding of Low-Rank Approximation (Poster)
Kazu Ghalamkari, Mahito Sugiyama
22. Deep Riemannian Manifold Learning (Poster)
Aaron Lou, Maximillian Nickel, Brandon Amos
23. Directional Graph Networks (Contributed Talk)
Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, Pietro Liò
24. A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups (Contributed Talk)
Piotr Kicki, Piotr Skrzypczynski, Mete Ozay
25. Leveraging Smooth Manifolds for Lexical Semantic Change Detection across Corpora (Poster)
Anmol Goel, Ponnurangam Kumaraguru
26. Tree Covers: An Alternative to Metric Embeddings (Poster)
Roshni Sahoo, Ines Chami, Christopher Ré
27. Deep Networks and the Multiple Manifold Problem (Poster)
Sam Buchanan, Dar Gilboa, John Wright
29. Extendable and invertible manifold learning with geometry regularized autoencoders (Poster)
Andres Duque, Sacha Morin, Guy Wolf, Kevin Moon
30. A Metric for Linear Symmetry-Based Disentanglement (Poster)
Luis Armando Pérez Rey, Loek Tonnaer, Vlado Menkovski, Mike Holenderski, Jim Portegies