An Exploration of Conditioning Methods in Graph Neural Networks

3 May 2023

Papers citing "An Exploration of Conditioning Methods in Graph Neural Networks"

5 / 5 papers shown

Title
Grounding Continuous Representations in Geometry: Equivariant Neural Fields David R. Wessels David M. Knigge Samuele Papa Riccardo Valperga Sharvaree P. Vadgama E. Gavves Erik J. Bekkers 47 7 0 09 Jun 2024
Fast, Expressive SE $(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space Erik J. Bekkers Sharvaree P. Vadgama Rob D. Hesselink P. A. V. D. Linden David W. Romero 18 24 0 04 Oct 2023
From data to functa: Your data point is a function and you can treat it like one Emilien Dupont Hyunjik Kim S. M. Ali Eslami Danilo Jimenez Rezende Dan Rosenbaum TDI 3DPC 178 139 0 28 Jan 2022
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
Xception: Deep Learning with Depthwise Separable Convolutions François Chollet MDE BDL PINN 206 14,368 0 07 Oct 2016