mlsanityData to 7 Oct 2026

Group Equivariant Conditional Neural Processes

arXiv 2102.08759 ↗First posted 17 Feb 2021cs.LG stat.MLcited by 16

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Abstract (arXiv)

We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equivariance, provides a way to consider the symmetry of real-world data. We give a decomposition theorem for permutation-invariant and group-equivariant maps, which leads us to construct EquivCNPs with an infinite-dimensional latent space to handle group symmetries. In this paper, we build architecture using Lie group convolutional layers for practical implementation. We show that EquivCNP with translation equivariance achieves comparable performance to conventional CNPs in a 1D regression task. Moreover, we demonstrate that incorporating an appropriate Lie group equivariance, EquivCNP is capable of zero-shot generalization for an image-completion task by selecting an appropriate Lie group equivariance.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 29 of its 35 references that are in the map. First posted on arXiv 17 Feb 2021.

Builds on

29 of 35 references.

References · 29

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17 more
  1. 2018On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groupscited by 296
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 5; 2024: 3; 2025: 2; 2026: 1.

16 papers.

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  1. 2024Learning to Defer to a Population: A Meta-Learning Approachcited by 26
  2. 2022Practical Conditional Neural Processes Via Tractable Dependent Predictionscited by 25
  3. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  4. 2023Practical Equivariances via Relational Conditional Neural Processescited by 13
  5. 2022Contrastive Conditional Neural Processescited by 11
  6. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  7. 2021Efficient Gaussian Neural Processes for Regressioncited by 10
  8. 2024Approximately Equivariant Neural Processescited by 8
  9. 2023Deep Stochastic Processes via Functional Markov Transition Operatorscited by 7
  10. 2025Bridge the Inference Gaps of Neural Processes via Expectation Maximizationcited by 7
  11. 2024Spectral Convolutional Conditional Neural Processescited by 4
  12. 2022Bayesian Convolutional Deep Sets with Task-Dependent Stationary Priorcited by 2
4 more
  1. 2023Exploiting Inferential Structure in Neural Processescited by 2
  2. 2025CLIPSym: Delving into Symmetry Detection with CLIPcited by 2
  3. 2023Autoencoding Conditional Neural Processes for Representation Learningcited by 0
  4. 2026Revisiting Neural Processes via Fourier Transform and Volterra Seriescited by 0