Symmetry meets AI
Not on the map. Why?
Abstract (arXiv)
We explore whether Neural Networks (NNs) can it discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a it decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 10 Mar 2021.
Builds on
5 of 14 references.
References · 5
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019An Introduction to Variational Autoencoderscited by 997
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
- 2018A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decaycited by 223
- 2020Detecting Symmetries with Neural Networkscited by 33
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 6; 2024: 1; 2025: 2; 2026: 1.
13 papers.
Sort:
- 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
- 2023Discovering Sparse Representations of Lie Groups with Machine Learningcited by 8
- 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6cited by 6
- 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetriescited by 6
- 2023Oracle-Preserving Latent Flowscited by 5
- 2021Beyond permutation equivariance in graph networkscited by 2
- 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
- 2021Symmetry-driven graph neural networkscited by 1
- 2021Encoding Involutory Invariances in Neural Networkscited by 1
- 2024Data-driven discovery of self-similarity using neural networkscited by 1
- 2025Learning symmetries in datasetscited by 1
- 2026Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Data2 Feb 2026
- 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction26 Sep 2025
- 2025Learning symmetries in datasets7 Apr 2025
- 2024Data-driven discovery of self-similarity using neural networks6 Jun 2024
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Review15 Dec 2023
- 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetries14 Sep 2023
- 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E610 Jul 2023
- 2023Discovering Sparse Representations of Lie Groups with Machine Learning10 Feb 2023
- 2023Oracle-Preserving Latent Flows2 Feb 2023
- 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles13 Jan 2023
- 2021Encoding Involutory Invariances in Neural Networks7 Jun 2021
- 2021Symmetry-driven graph neural networks28 May 2021
1 more
- 2021Beyond permutation equivariance in graph networks25 Mar 2021
- 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
- 2023Discovering Sparse Representations of Lie Groups with Machine Learningcited by 8
- 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6cited by 6
- 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetriescited by 6
- 2023Oracle-Preserving Latent Flowscited by 5
- 2021Beyond permutation equivariance in graph networkscited by 2
- 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
- 2021Symmetry-driven graph neural networkscited by 1
- 2021Encoding Involutory Invariances in Neural Networkscited by 1
- 2024Data-driven discovery of self-similarity using neural networkscited by 1
- 2025Learning symmetries in datasetscited by 1
12 most cited of 13 All 13 (JSON)
- 2026Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Data2 Feb 2026
- 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction26 Sep 2025
- 2025Learning symmetries in datasets7 Apr 2025
- 2024Data-driven discovery of self-similarity using neural networks6 Jun 2024
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Review15 Dec 2023
- 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetries14 Sep 2023
- 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E610 Jul 2023
- 2023Discovering Sparse Representations of Lie Groups with Machine Learning10 Feb 2023
- 2023Oracle-Preserving Latent Flows2 Feb 2023
- 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles13 Jan 2023
- 2021Encoding Involutory Invariances in Neural Networks7 Jun 2021
- 2021Symmetry-driven graph neural networks28 May 2021
12 newest of 13 All 13 (JSON)