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An Exploration of Conditioning Methods in Graph Neural Networks

An Exploration of Conditioning Methods in Graph Neural Networks

3 May 2023
Yeskendir Koishekenov
Erik J. Bekkers
    AI4CE
ArXivPDFHTML

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

4 / 4 papers shown
Title
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
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
31
7
0
09 Jun 2024
From data to functa: Your data point is a function and you can treat it
  like one
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
154
136
0
28 Jan 2022
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
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
183
1,218
0
08 Jan 2021
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
MDE
BDL
PINN
190
14,190
0
07 Oct 2016
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