mlsanityData to 7 Oct 2026

Bootstrapping Neural Processes

arXiv 2008.02956 ↗First posted 7 Aug 2020cs.LG stat.MLcited by 40

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Abstract (arXiv)

Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stochastic processes with neural networks. Given a data stream, NP learns a stochastic process that best describes the data. While this "data-driven" way of learning stochastic processes has proven to handle various types of data, NPs still rely on an assumption that uncertainty in stochastic processes is modeled by a single latent variable, which potentially limits the flexibility. To this end, we propose the Boostrapping Neural Process (BNP), a novel extension of the NP family using the bootstrap. The bootstrap is a classical data-driven technique for estimating uncertainty, which allows BNP to learn the stochasticity in NPs without assuming a particular form. We demonstrate the efficacy of BNP on various types of data and its robustness in the presence of model-data mismatch.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 8; 2023: 11; 2024: 7; 2025: 8; 2026: 4.

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  1. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 126
  2. 2022Latent Bottlenecked Attentive Neural Processescited by 26
  3. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  4. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  5. 2022Neural Processes with Stochastic Attention: Paying more attention to the context datasetcited by 16
  6. 2024Stochastic Optimal Control for Diffusion Bridges in Function Spacescited by 16
  7. 2021Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognitioncited by 15
  8. 2023Martingale Posterior Neural Processescited by 15
  9. 2023Versatile Neural Processes for Learning Implicit Neural Representationscited by 11
  10. 2024Inter-Domain Mixup for Semi-Supervised Domain Adaptationcited by 10
  11. 2023Memory Efficient Neural Processes via Constant Memory Attention Blockcited by 9
  12. 2025Bridge the Inference Gaps of Neural Processes via Expectation Maximizationcited by 7
28 more
  1. 2022Semi-Parametric Inducing Point Networks and Neural Processescited by 6
  2. 2025Flow Matching Neural Processescited by 6
  3. 2023NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentationcited by 5
  4. 2025Dimension Agnostic Neural Processescited by 5
  5. 2023Entity Aware Modelling: A Surveycited by 4
  6. 2022NIERT: Accurate Numerical Interpolation through Unifying Scattered Data Representations using Transformer Encodercited by 3
  7. 2022Compositional Law Parsing with Latent Random Functionscited by 3
  8. 2023Adaptive Conditional Quantile Neural Processescited by 3
  9. 2024Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processescited by 3
  10. 2024Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learningcited by 3
  11. 2025Distance-informed Neural Processescited by 3
  12. 2023Taylorformer: Probabilistic Modelling for Random Processes including Time Seriescited by 2
  13. 2023Exploiting Inferential Structure in Neural Processescited by 2
  14. 2023Tree Cross Attentioncited by 2
  15. 2025Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processescited by 2
  16. 2023NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learningcited by 1
  17. 2023Constant Memory Attention Blockcited by 1
  18. 2024R'enyi Neural Processescited by 1
  19. 2024Robust Neural Processes for Noisy Datacited by 1
  20. 2025Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodingscited by 1
  21. 2021Meta-learning Amidst Heterogeneity and Ambiguitycited by 0
  22. 2024Exploring Probabilistic Models for Semi-supervised Learningcited by 0
  23. 2025GraphPPD: Posterior Predictive Modelling for Graph-Level Inferencecited by 0
  24. 2025Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructionscited by 0
  25. 2026Physics-informed Diffusion Generation for Geomagnetic Map Interpolationcited by 0
  26. 2026Amortising Inference and Meta-Learning Priors in Neural Networkscited by 0
  27. 2026Spectral Transformer Neural Processescited by 0
  28. 2026Revisiting Neural Processes via Fourier Transform and Volterra Seriescited by 0