Paper Citation Record · LEDGER

Calibration Prediction Interval for Non-parametric Regression and Neural Networks

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2509.02735.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.02735 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:40:43.297529Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b781a55-6386-4c31-8b3b-0d353f6a2ee9 · outbound

This paper cites Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks

Reference 1

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Observation a76c3823-94a1-4279-b0ba-2d88693eef99 · outbound

This paper cites On deep learning as a remedy for the curse of dimensionality in nonparametric regression.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks On deep learning as a remedy for the curse of dimensionality in nonparametric regression

Reference 2

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Observation cc0609e5-917c-4c7e-b3b1-71c03fa04aa6 · outbound

This paper cites Nonparametric estimates of regression quantiles and their local bahadur representation.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric estimates of regression quantiles and their local bahadur representation

Reference 3

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Observation 189054da-1067-41d4-bb5d-dc2756b47af8 · outbound

This paper cites Modeling wine preferences by data mining from physicochemical properties.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Modeling wine preferences by data mining from physicochemical properties

Reference 4

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Observation 7452e962-d166-416d-8953-4dc994ea4778 · outbound

This paper cites Nonparametric estimation of the conditional distribution at regression boundary points.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric estimation of the conditional distribution at regression boundary points

Reference 5

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Observation cef20a8e-c3c3-4a58-b2bd-6a8f0d45562d · outbound

This paper cites Deep neural networks for estimation and inference.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep neural networks for estimation and inference

Reference 6

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Observation 697acbad-3e5a-48f8-a463-fa73def8c01b · outbound

This paper cites On calibration of modern neural networks.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks On calibration of modern neural networks

Reference 7

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Observation a7b0f53f-3044-4a9e-81e2-09c0ed8f0b93 · outbound

This paper cites Nonparametric estimation of smooth conditional distributions.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric estimation of smooth conditional distributions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:40:43.633102Z

Source-reported events for the cited work

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Observation a27a757b-d260-45eb-a5b3-fe6d6a206d57 · outbound

This paper cites Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors

Reference 9

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Observation 1cb3b696-2264-4f61-addb-8bd520065c4f · outbound

This paper cites Lower upper bound estimation method for construction of neural network-based prediction intervals.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Lower upper bound estimation method for construction of neural network-based prediction intervals

Reference 10

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Source-reported events for the cited work

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Observation 5793427e-797d-41ee-beba-3287ed9311fc · outbound

This paper cites Quantile regression: 40 years on.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Quantile regression: 40 years on

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:40:43.608929Z

Source-reported events for the cited work

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Observation 17eeb198-99ca-4c4f-b4cf-77bba7188a70 · outbound

This paper cites Distribution-free prediction bands for non-parametric regression.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Distribution-free prediction bands for non-parametric regression

Reference 12

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Observation 85d15310-5e59-4607-9f67-750bd5dfa933 · outbound

This paper cites Distribution-free predictive inference for regression.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Distribution-free predictive inference for regression

Reference 13

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Observation 81ac6b22-b063-4cdd-b5d3-4e924519799b · outbound

This paper cites Nonparametric econometrics: theory and practice.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric econometrics: theory and practice

Reference 14

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Observation 6885321c-a459-4fb9-88e3-d6bbcd7bf433 · outbound

This paper cites Wasserstein Generative Learning of Conditional Distribution.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Wasserstein Generative Learning of Conditional Distribution

Reference 15

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Observation be21a5a3-0302-4274-b97f-02d9d3001adc · outbound

This paper cites Prediction intervals for Deep Neural Networks.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Prediction intervals for Deep Neural Networks

Reference 16

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Observation 4bee6688-622f-4e6f-a73e-2d13cbb3550a · outbound

This paper cites Adaptive approximation and generalization of deep neural network with intrinsic dimensionality.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Adaptive approximation and generalization of deep neural network with intrinsic dimensionality

Reference 17

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Source-reported events for the cited work

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Observation 7aeb5349-e4ab-4e04-a40e-a9059ada258c · outbound

This paper cites Ospool, 2006.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Ospool, 2006

Reference 18

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Observation 6c087eb6-e8d2-4a6b-9ff5-10bbccf0a877 · outbound

This paper cites Open science data federation, 2015.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Open science data federation, 2015

Reference 19

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Source-reported events for the cited work

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Observation 72225ef8-ddf8-4136-a96c-6cc6a61c7143 · outbound

This paper cites Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks

Reference 20

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Observation 0267a2e0-67ef-4c56-b761-eb0391670f72 · outbound

This paper cites High-quality prediction intervals for deep learning: A distribution-free, ensembled approach.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks High-quality prediction intervals for deep learning: A distribution-free, ensembled approach

Reference 21

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Observation 21ea3e33-c7f8-4cd9-8aca-86eb9cffa99e · outbound

This paper cites Model-Free Prediction and Regression: A Transformation-Based Approach to Inference.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Model-Free Prediction and Regression: A Transformation-Based Approach to Inference

Reference 22

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Observation a8ad0e8c-59a5-4b83-8833-2e47735e81f8 · outbound

This paper cites Scalable subsampling: computation, aggregation and inference.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Scalable subsampling: computation, aggregation and inference

Reference 23

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Observation e612730c-327a-43ea-b4aa-9d33122fce34 · outbound

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Calibration Prediction Interval for Non-parametric Regression and Neural Networks The open science grid

Reference 24

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Observation 9daf15be-b80f-4902-b132-ba6a016ebb47 · outbound

This paper cites Deep ReLU network approximation of functions on a manifold.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep ReLU network approximation of functions on a manifold

Reference 25

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Observation 60f8d43f-4aaa-4391-b567-31b0d253340d · outbound

This paper cites Nonparametric regression using deep neural networks with relu activation function.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric regression using deep neural networks with relu activation function

Reference 26

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Observation 13e26ff5-ac73-4619-ac64-e034f86ccd48 · outbound

This paper cites The pilot way to grid resources using glideinwms.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks The pilot way to grid resources using glideinwms

Reference 27

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Observation 2b1a639c-2ed2-4535-abcc-92faa2d45545 · outbound

This paper cites Deep Network Approximation Characterized by Number of Neurons.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep Network Approximation Characterized by Number of Neurons

Reference 28

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Observation 9c2154a2-78a1-4f4f-a39e-ad95fd8f5f55 · outbound

This paper cites PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction

Reference 29

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Observation ffb103f4-4cc4-486d-b42a-a42f1ac83478 · outbound

This paper cites Single-model uncertainties for deep learning.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Single-model uncertainties for deep learning

Reference 30

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Observation 66179fb3-5f0a-4245-992d-cdbd5db88f73 · outbound

This paper cites Nonparametric quantile estimation.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Nonparametric quantile estimation

Reference 31

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Source-reported events for the cited work

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Observation 2574537f-9d4d-4d78-9d41-48f7dcab53e3 · outbound

This paper cites Methods to compute prediction intervals: A review and new results.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Methods to compute prediction intervals: A review and new results

Reference 32

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Source-reported events for the cited work

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Observation 7c81cfdd-5603-4500-89f7-7335acf5ad1c · outbound

This paper cites Model-free Bootstrap and Conformal Prediction in Regression: Conditionality, Conjecture Testing, and Pertinent Prediction Intervals.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Model-free Bootstrap and Conformal Prediction in Regression: Conditionality, Conjecture Testing, and Pertinent Prediction Intervals

Reference 33

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Observation 4425d1d2-3e2b-4035-81d9-b8ae0837518d · outbound

This paper cites Deep Limit Model-free Prediction in Regression.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Deep Limit Model-free Prediction in Regression

Reference 34

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Source-reported events for the cited work

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Observation aa8c0a98-8228-4c17-bddd-dc8826ca1c2d · outbound

This paper cites Scalable subsampling inference for deep neural networks.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks Scalable subsampling inference for deep neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:40:43.452242Z

Source-reported events for the cited work

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Observation 9b2147bd-5a66-4855-a6f9-6512079fd64f · outbound

This paper cites A deep generative approach to conditional sampling.

Calibration Prediction Interval for Non-parametric Regression and Neural Networks A deep generative approach to conditional sampling

Reference 36

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