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Universal approximations of permutation invariant/equivariant functions
  by deep neural networks

Universal approximations of permutation invariant/equivariant functions by deep neural networks

5 March 2019
Akiyoshi Sannai
Yuuki Takai
Matthieu Cordonnier
ArXivPDFHTML

Papers citing "Universal approximations of permutation invariant/equivariant functions by deep neural networks"

14 / 14 papers shown
Title
Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension
Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension
Miguel Abreu
Luis Paulo Reis
N. Lau
41
5
0
06 Sep 2023
On the Approximation and Complexity of Deep Neural Networks to Invariant
  Functions
On the Approximation and Complexity of Deep Neural Networks to Invariant Functions
Gao Zhang
Jin-Hui Wu
Shao-Qun Zhang
16
0
0
27 Oct 2022
Deep Neural Network Approximation of Invariant Functions through
  Dynamical Systems
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
Qianxiao Li
T. Lin
Zuowei Shen
24
6
0
18 Aug 2022
Low Dimensional Invariant Embeddings for Universal Geometric Learning
Low Dimensional Invariant Embeddings for Universal Geometric Learning
Nadav Dym
S. Gortler
29
39
0
05 May 2022
Permutation Invariant Representations with Applications to Graph Deep
  Learning
Permutation Invariant Representations with Applications to Graph Deep Learning
R. Balan
Naveed Haghani
M. Singh
28
25
0
14 Mar 2022
Explicitly antisymmetrized neural network layers for variational Monte
  Carlo simulation
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
Jeffmin Lin
Gil Goldshlager
Lin Lin
40
22
0
07 Dec 2021
Capacity of Group-invariant Linear Readouts from Equivariant
  Representations: How Many Objects can be Linearly Classified Under All
  Possible Views?
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
M. Farrell
Blake Bordelon
Shubhendu Trivedi
Cengiz Pehlevan
18
5
0
14 Oct 2021
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph
  Representation Learning
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning
Marinos Poiitis
Pavlos Sermpezis
Athena Vakali
28
0
0
06 Sep 2021
Fixed-Dimensional and Permutation Invariant State Representation of
  Autonomous Driving
Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving
Jingliang Duan
Dongjie Yu
Shengbo Eben Li
Wenxuan Wang
Yangang Ren
Ziyu Lin
B. Cheng
22
10
0
24 May 2021
Symmetry reduction for deep reinforcement learning active control of
  chaotic spatiotemporal dynamics
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics
Kevin Zeng
M. Graham
AI4CE
11
22
0
09 Apr 2021
Universal Equivariant Multilayer Perceptrons
Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
100
48
0
07 Feb 2020
Are Transformers universal approximators of sequence-to-sequence
  functions?
Are Transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun
Srinadh Bhojanapalli
A. S. Rawat
Sashank J. Reddi
Sanjiv Kumar
8
335
0
20 Dec 2019
A Simple Proof of the Universality of Invariant/Equivariant Graph Neural
  Networks
A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks
Takanori Maehara
Hoang NT
24
29
0
09 Oct 2019
On Universal Equivariant Set Networks
On Universal Equivariant Set Networks
Nimrod Segol
Y. Lipman
3DPC
22
63
0
06 Oct 2019
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