mlsanityData to 14 Oct 2026

Distance-informed Neural Processes

arXiv 2508.18903 ↗First posted 26 Aug 2025cs.LG cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a global latent variable and struggle with uncertainty calibration and capturing local data dependencies. DNP addresses these limitations by introducing a global latent variable to model task-level variations and a local latent variable to capture input similarity within a distance-preserving latent space. This is achieved through bi-Lipschitz regularization, which bounds distortions in input relationships and encourages the preservation of relative distances in the latent space. This modeling approach allows DNP to produce better-calibrated uncertainty estimates and more effectively distinguish in- from out-of-distribution data. Empirical results demonstrate that DNP achieves strong predictive performance and improved uncertainty calibration across regression and classification tasks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 26 Aug 2025.

Builds on

44 of 71 references.

References · 44

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2017On Calibration of Modern Neural Networkscited by 4,960
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  7. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  8. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  9. 2017Improved Training of Wasserstein GANscited by 2,235
  10. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
  11. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  12. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
32 more
  1. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  2. 2019An Introduction to Variational Autoencoderscited by 1,000
  3. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  4. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  5. 2015Deep Kernel Learningcited by 445
  6. 2018Conditional Neural Processescited by 426
  7. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 422
  8. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  9. 2018Invertible Residual Networkscited by 365
  10. 2018Neural Processescited by 311
  11. 2019Attentive Neural Processescited by 283
  12. 2020Sparse Sinkhorn Attentioncited by 251
  13. 2018An Introduction to Deep Reinforcement Learningcited by 206
  14. 2017Spectral Norm Regularization for Improving the Generalizability of Deep Learningcited by 182
  15. 2019Convolutional Conditional Neural Processescited by 142
  16. 2018i-RevNet: Deep Invertible Networkscited by 137
  17. 2019Robust Learning with Jacobian Regularizationcited by 129
  18. 2022Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modelingcited by 127
  19. 2016Neural Photo Editing with Introspective Adversarial Networkscited by 109
  20. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  21. 2020Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processescited by 76
  22. 2022Rank-N-Contrast: Learning Continuous Representations for Regressioncited by 76
  23. 2019A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inferencecited by 51
  24. 2019The Functional Neural Processcited by 43
  25. 2020Bootstrapping Neural Processescited by 40
  26. 2020Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variablescited by 33
  27. 2020Function Contrastive Learning of Transferable Meta-Representationscited by 19
  28. 2023Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifierscited by 10
  29. 2025Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelscited by 9
  30. 2025Bridge the Inference Gaps of Neural Processes via Expectation Maximizationcited by 8
  31. 2023Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrievalcited by 3
  32. 2023Exploiting Inferential Structure in Neural Processescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstructioncited by 0
  2. 2026Spectral Transformer Neural Processescited by 0
  3. 2026APIC: Amortized Physics-Informed Calibration using Neural Processescited by 0