mlsanityData to 7 Oct 2026

Oracle-Preserving Latent Flows

arXiv 2302.00806 ↗First posted 2 Feb 2023cs.LG hep-ph math.GRcited by 5

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Abstract (arXiv)

We develop a deep learning methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset. The symmetry transformations and the corresponding generators are modeled with fully connected neural networks trained with a specially constructed loss function ensuring the desired symmetry properties. The two new elements in this work are the use of a reduced-dimensionality latent space and the generalization to transformations invariant with respect to high-dimensional oracles. The method is demonstrated with several examples on the MNIST digit dataset.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

34 of 56 references.

References · 34

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22 more
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Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 1.

5 papers.

  1. 2023Discovering Sparse Representations of Lie Groups with Machine Learningcited by 8
  2. 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6cited by 6
  3. 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetriescited by 6
  4. 2023A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networkscited by 6
  5. 2024Lie-Equivariant Quantum Graph Neural Networkscited by 0