mlsanityData to 7 Oct 2026

Sequential Neural Processes

arXiv 1906.10264 ↗First posted 24 Jun 2019cs.LG stat.MLcited by 55

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Abstract (arXiv)

Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes. However, a large class of problems comprises underlying temporal dependency structures in a sequence of stochastic processes that Neural Processes (NP) do not explicitly consider. In this paper, we propose Sequential Neural Processes (SNP) which incorporates a temporal state-transition model of stochastic processes and thus extends its modeling capabilities to dynamic stochastic processes. In applying SNP to dynamic 3D scene modeling, we introduce the Temporal Generative Query Networks. To our knowledge, this is the first 4D model that can deal with the temporal dynamics of 3D scenes. In experiments, we evaluate the proposed methods in dynamic (non-stationary) regression and 4D scene inference and rendering.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 15; 2023: 11; 2024: 9; 2025: 3; 2026: 4.

55 papers.

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  1. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 126
  2. 2021Neural ODE Processescited by 57
  3. 2022Generative Pretraining for Black-Box Optimizationcited by 36
  4. 2021Convolutional conditional neural processes for local climate downscalingcited by 29
  5. 2022Latent Bottlenecked Attentive Neural Processescited by 26
  6. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  7. 2022Spectral Diffusion Processescited by 24
  8. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  9. 2023ExPT: Synthetic Pretraining for Few-Shot Experimental Designcited by 22
  10. 2023Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completioncited by 19
  11. 2023Graph Neural Processes for Spatio-Temporal Extrapolationcited by 18
  12. 2022Neural Processes with Stochastic Attention: Paying more attention to the context datasetcited by 16
38 more
  1. 2022A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloudcited by 16
  2. 2021Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognitioncited by 15
  3. 2022Multi-fidelity Hierarchical Neural Processescited by 15
  4. 2022Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphscited by 13
  5. 2024Zero-shot Imputation with Foundation Inference Models for Dynamical Systemscited by 13
  6. 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modelingcited by 13
  7. 2024Spatio-Temporal Field Neural Networks for Air Quality Inferencecited by 12
  8. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  9. 2023Disentangled Multi-Fidelity Deep Bayesian Active Learningcited by 11
  10. 2021Object-Centric Representation Learning with Generative Spatial-Temporal Factorizationcited by 10
  11. 2023Geometric Neural Diffusion Processescited by 10
  12. 2023Memory Efficient Neural Processes via Constant Memory Attention Blockcited by 9
  13. 2021Evidential Turing Processescited by 8
  14. 2024Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Trackingcited by 8
  15. 2021Deep Bayesian Active Learning for Accelerating Stochastic Simulationcited by 7
  16. 2021Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Futurecited by 7
  17. 2021Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Modelscited by 6
  18. 2021Social Processes: Self-Supervised Meta-Learning over Conversational Groups for Forecasting Nonverbal Social Cuescited by 6
  19. 2024Spatially-Aware Transformer for Embodied Agentscited by 5
  20. 2022Convolutional Neural Processes for Inpainting Satellite Imagescited by 4
  21. 2023Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approachcited by 4
  22. 2023Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategiescited by 4
  23. 2021GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Datacited by 3
  24. 2021Global Convolutional Neural Processescited by 3
  25. 2022Compositional Law Parsing with Latent Random Functionscited by 3
  26. 2022Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learningcited by 3
  27. 2024Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processescited by 3
  28. 2024System-Aware Neural ODE Processes for Few-Shot Bayesian Optimizationcited by 3
  29. 2024Federated Neural Nonparametric Point Processescited by 3
  30. 2022Bayesian Convolutional Deep Sets with Task-Dependent Stationary Priorcited by 2
  31. 2024Parallelized Spatiotemporal Bindingcited by 2
  32. 2025AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networkscited by 2
  33. 2022Continual Learning of Multi-modal Dynamics with External Memorycited by 1
  34. 2023NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learningcited by 1
  35. 2023Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashingcited by 1
  36. 2025Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networkscited by 1
  37. 2021Meta-learning Amidst Heterogeneity and Ambiguitycited by 0
  38. 2021Multi-Task Neural Processescited by 0

50 most cited of 55 All 55 (JSON)