Low Dimensional Invariant Embeddings for Universal Geometric Learning

5 May 2022

Papers citing "Low Dimensional Invariant Embeddings for Universal Geometric Learning"

27 / 27 papers shown

Title
Fourier Sliced-Wasserstein Embedding for Multisets and Measures Tal Amir Nadav Dym 48 2 0 03 Apr 2025
Bi-Lipschitz Ansatz for Anti-Symmetric Functions Nadav Dym Jianfeng Lu Matan Mizrachi 40 1 0 06 Mar 2025
Learning with Exact Invariances in Polynomial Time Ashkan Soleymani B. Tahmasebi Stefanie Jegelka P. Jaillet 78 0 0 27 Feb 2025
Stability of sorting based embeddings Radu Balan Efstratios Tsoukanis Matthias Wellershoff 23 2 0 07 Oct 2024
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models Theo Putterman Derek Lim Yoav Gelberg Stefanie Jegelka Haggai Maron AI4CE 43 5 0 05 Oct 2024
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks Mitchell Keren Taraday Almog David Chaim Baskin 28 0 0 28 Sep 2024
On the Expressive Power of Sparse Geometric MPNNs Yonatan Sverdlov Nadav Dym 48 1 0 02 Jul 2024
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency Yuchao Lin Jacob Helwig Shurui Gui Shuiwang Ji 42 7 0 11 Jun 2024
On the Hölder Stability of Multiset and Graph Neural Networks Yair Davidson Nadav Dym 51 0 0 11 Jun 2024
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control Haoyu Wang Xiaoyu Tan Xihe Qiu Chao Qu 48 2 0 22 Mar 2024
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 36 2 0 07 Feb 2024
A Characterization Theorem for Equivariant Networks with Point-wise Activations Marco Pacini Xiaowen Dong Bruno Lepri G. Santin 44 2 0 17 Jan 2024
Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim Joshua Robinson Stefanie Jegelka Haggai Maron 71 16 0 04 Dec 2023
Learning Symmetrization for Equivariance with Orbit Distance Minimization Tien Dat Nguyen Jinwoo Kim Hongseok Yang Seunghoon Hong 32 3 0 13 Nov 2023
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions Samantha Chen Yusu Wang 20 3 0 01 Aug 2023
Polynomial Width is Sufficient for Set Representation with High-dimensional Features Peihao Wang Shenghao Yang Shu Li Zhangyang Wang Pan Li 19 3 0 08 Jul 2023
Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem Tal Amir S. Gortler Ilai Avni Ravi Ravina Nadav Dym 97 24 0 10 Jun 2023
Complete Neural Networks for Complete Euclidean Graphs Snir Hordan Tal Amir S. Gortler Nadav Dym 3DPC 29 5 0 31 Jan 2023
Towards fully covariant machine learning Soledad Villar D. Hogg Weichi Yao George A. Kevrekidis Bernhard Schölkopf AI4CE 38 10 0 31 Jan 2023
Machine learning and invariant theory Ben Blum-Smith Soledad Villar AI4CE 36 16 0 29 Sep 2022
Group-invariant max filtering Jameson Cahill Joseph W. Iverson D. Mixon Dan Packer 27 21 0 27 May 2022
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design Xiran Fan Chun-Hao Yang B. Vemuri 34 18 0 03 Dec 2021
A simple equivariant machine learning method for dynamics based on scalars Weichi Yao Kate Storey-Fisher D. Hogg Soledad Villar AI4CE 39 9 0 07 Oct 2021
Frame Averaging for Invariant and Equivariant Network Design Omri Puny Matan Atzmon Heli Ben-Hamu Ishan Misra Aditya Grover Edward James Smith Y. Lipman FedML 49 128 0 07 Oct 2021
Vector Neurons: A General Framework for SO(3)-Equivariant Networks Congyue Deng Or Litany Yueqi Duan A. Poulenard Andrea Tagliasacchi Leonidas J. Guibas 3DPC 111 315 0 25 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 206 1,240 0 08 Jan 2021
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,103 0 02 Dec 2016