mlsanityData to 14 Oct 2026

Practical Equivariances via Relational Conditional Neural Processes

arXiv 2306.10915 ↗First posted 19 Jun 2023stat.ML cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatio-temporal modeling, Bayesian Optimization and continuous control, inherently contain equivariances -- for example to translation -- which the model can exploit for maximal performance. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions. In this work, we propose Relational Conditional Neural Processes (RCNPs), an effective approach to incorporate equivariances into any neural process model. Our proposed method extends the applicability and impact of equivariant neural processes to higher dimensions. We empirically demonstrate the competitive performance of RCNPs on a large array of tasks naturally containing equivariances.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 19 Jun 2023.

Builds on

27 of 44 references.

References · 27

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  3. 2017Deep Setscited by 1,421
  4. 2016Group Equivariant Convolutional Networkscited by 1,165
  5. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  6. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  7. 2015Deep Kernel Learningcited by 445
  8. 2018Conditional Neural Processescited by 426
  9. 2017The Expressive Power of Neural Networks: A View from the Widthcited by 320
  10. 2018Neural Processescited by 311
  11. 2018On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groupscited by 300
  12. 2019Attentive Neural Processescited by 283
15 more
  1. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  2. 2019Convolutional Conditional Neural Processescited by 142
  3. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 127
  4. 2020Building powerful and equivariant graph neural networks with structural message-passingcited by 97
  5. 2019On the Limitations of Representing Functions on Setscited by 90
  6. 2020Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processescited by 76
  7. 2021Universal Approximation of Functions on Setscited by 62
  8. 2021The Gaussian Neural Processcited by 34
  9. 2023Autoregressive Conditional Neural Processescited by 33
  10. 2020Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processescited by 29
  11. 2022Practical Conditional Neural Processes Via Tractable Dependent Predictionscited by 25
  12. 2018Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completioncited by 18
  13. 2021Model-based Meta Reinforcement Learning using Graph Structured Surrogate Modelscited by 17
  14. 2021Group Equivariant Conditional Neural Processescited by 16
  15. 2021Kernel Identification Through Transformerscited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 2.

13 papers.

Sort:

  1. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 23
  2. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 21
  3. 2024Translation Equivariant Transformer Neural Processescited by 13
  4. 2024Approximately Equivariant Neural Processescited by 8
  5. 2025PABBO: Preferential Amortized Black-Box Optimizationcited by 8
  6. 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisitioncited by 8
  7. 2024Spectral Convolutional Conditional Neural Processescited by 4
  8. 2025Meta-Statistical Learning: Supervised Learning of Statistical Estimatorscited by 3
  9. 2024Noise-Aware Differentially Private Regression via Meta-Learningcited by 2
  10. 2024R'enyi Neural Processescited by 1
  11. 2025MIST: Mutual Information Estimation Via Supervised Trainingcited by 1
  12. 2026From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimationcited by 0
1 more
  1. 2026Revisiting Neural Processes via Fourier Transform and Volterra Seriescited by 0