mlsanityData to 7 Oct 2026

Neural Processes

arXiv 1807.01622 ↗First posted 4 Jul 2018cs.LG stat.MLcited by 307

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Like GPs, NPs define distributions over functions, are capable of rapid adaptation to new observations, and can estimate the uncertainty in their predictions. Like NNs, NPs are computationally efficient during training and evaluation but also learn to adapt their priors to data. We demonstrate the performance of NPs on a range of learning tasks, including regression and optimisation, and compare and contrast with related models in the literature.

Key facts. Cited by 307 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2018.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 55; 2022: 45; 2023: 60; 2024: 50; 2025: 58; 2026: 38.

307 papers.

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  1. 2022What Can Transformers Learn In-Context? A Case Study of Simple Function Classescited by 700
  2. 2021Transformers Can Do Bayesian Inferencecited by 338
  3. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 245
  4. 2022From data to functa: Your data point is a function and you can treat it like onecited by 183
  5. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 126
  6. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
  7. 2022CARD: Classification and Regression Diffusion Modelscited by 102
  8. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  9. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  10. 2021Generative Models as Distributions of Functionscited by 89
  11. 2021Conservative Objective Models for Effective Offline Model-Based Optimizationcited by 87
  12. 2023Score-based Diffusion Models in Function Spacecited by 80
38 more
  1. 2021MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Imagescited by 78
  2. 2024Amortizing intractable inference in diffusion models for vision, language, and controlcited by 70
  3. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  4. 2021Neural ODE Processescited by 57
  5. 2024Feedback Efficient Online Fine-Tuning of Diffusion Modelscited by 52
  6. 2022Generative Adversarial Neural Operatorscited by 51
  7. 2022Neural Diffusion Processescited by 50
  8. 2022Diffusion Generative Models in Infinite Dimensionscited by 49
  9. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  10. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
  11. 2022Few-Shot Diffusion Modelscited by 37
  12. 2022Generative Pretraining for Black-Box Optimizationcited by 36
  13. 2024Online Adaptation of Language Models with a Memory of Amortized Contextscited by 35
  14. 2024Gradient-Free Generation for Hard-Constrained Systemscited by 35
  15. 2021The Gaussian Neural Processcited by 34
  16. 2021Context-Aware Safe Reinforcement Learning for Non-Stationary Environmentscited by 33
  17. 2024Active Evaluation Acquisition for Efficient LLM Benchmarkingcited by 33
  18. 2024Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodingscited by 31
  19. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  20. 2021Convolutional conditional neural processes for local climate downscalingcited by 29
  21. 2021Data-Driven Offline Optimization For Architecting Hardware Acceleratorscited by 29
  22. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  23. 2023Diffusion Probabilistic Fieldscited by 28
  24. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  25. 2022Latent Bottlenecked Attentive Neural Processescited by 26
  26. 2021NRTSI: Non-Recurrent Time Series Imputationcited by 25
  27. 2021Conditional Image Generation by Conditioning Variational Auto-Encoderscited by 25
  28. 2022ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitivescited by 25
  29. 2024CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Modelscited by 25
  30. 2021How Tight Can PAC-Bayes be in the Small Data Regime?cited by 24
  31. 2022Variable-Input Deep Operator Networkscited by 24
  32. 2022Spectral Diffusion Processescited by 24
  33. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  34. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  35. 2022COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognitioncited by 22
  36. 2023Variational Autoencoding Neural Operatorscited by 22
  37. 2023ExPT: Synthetic Pretraining for Few-Shot Experimental Designcited by 22
  38. 2024HyperFast: Instant Classification for Tabular Datacited by 22

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