Paper Citation Record · LEDGER

Dense ReLU Neural Networks for Temporal-spatial Model

As of 15 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2411.09961.

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

pith.paper-citation-record.v1
2411.09961 v8

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:31:12.534846Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T23:31:12.566264Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 4d147b32-bde1-45c8-b293-bd4e8c18d574 · outbound

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

Dense ReLU Neural Networks for Temporal-spatial Model Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks

Reference 1

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Observation 46c31007-b1fc-4c2b-af04-b5f00131eca1 · outbound

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

Dense ReLU Neural Networks for Temporal-spatial Model On deep learning as a remedy for the curse of dimensionality in nonparametric regression

Reference 2

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Observation bad227e6-758b-4715-8662-de3e83fea870 · outbound

This paper cites Concentration Inequalities: A Nonasymptotic Theory of Independence.

Dense ReLU Neural Networks for Temporal-spatial Model Concentration Inequalities: A Nonasymptotic Theory of Independence

Reference 3

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This paper cites Prediction in functional linear regression.

Dense ReLU Neural Networks for Temporal-spatial Model Prediction in functional linear regression

Reference 4

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Observation c6cb85f0-b5dd-4dbb-873a-5c6a54eeeaa8 · outbound

This paper cites Minimax and adaptive prediction for functional linear regression.

Dense ReLU Neural Networks for Temporal-spatial Model Minimax and adaptive prediction for functional linear regression

Reference 5

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This paper cites Functional linear model.

Dense ReLU Neural Networks for Temporal-spatial Model Functional linear model

Reference 6

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Observation 095024b3-0da6-4fdf-8ab4-d0bcdd28aee1 · outbound

This paper cites Testing hypotheses in the functional linear model.

Dense ReLU Neural Networks for Temporal-spatial Model Testing hypotheses in the functional linear model

Reference 7

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Observation 57778e39-c1d8-4662-948d-9eca938e1843 · outbound

This paper cites On total variation minimization and surface evolution using parametric maximum flows.

Dense ReLU Neural Networks for Temporal-spatial Model On total variation minimization and surface evolution using parametric maximum flows

Reference 8

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Observation d935c031-886d-4c22-a0b0-793feda906e6 · outbound

This paper cites Nonparametric regression on low-dimensional manifolds using deep relu networks: Function approximation and statistical recovery.

Dense ReLU Neural Networks for Temporal-spatial Model Nonparametric regression on low-dimensional manifolds using deep relu networks: Function approximation and statistical recovery

Reference 9

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Observation ae5f2ab8-20cd-41a3-b435-059caeb13697 · outbound

This paper cites A deep network construction that adapts to intrinsic dimensionality beyond the domain.

Dense ReLU Neural Networks for Temporal-spatial Model A deep network construction that adapts to intrinsic dimensionality beyond the domain

Reference 10

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Observation e6d5182b-dd96-4992-9bbc-1da08f141d0c · outbound

This paper cites Rates of convergence for nearest neighbor procedures.

Dense ReLU Neural Networks for Temporal-spatial Model Rates of convergence for nearest neighbor procedures

Reference 11

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Observation 7bddb7db-52c5-41b1-935a-023262898e1e · outbound

This paper cites High-dimensional data analysis: The curses and blessings of dimensionality.

Dense ReLU Neural Networks for Temporal-spatial Model High-dimensional data analysis: The curses and blessings of dimensionality

Reference 12

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Observation 7efbd7b6-3918-4c50-ad50-060603bde29b · outbound

This paper cites Mixing: properties and examples, volume 85.

Dense ReLU Neural Networks for Temporal-spatial Model Mixing: properties and examples, volume 85

Reference 13

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Observation 01214dec-6c4c-41d4-b700-93bd54d61de0 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Dense ReLU Neural Networks for Temporal-spatial Model Understanding the difficulty of training deep feedforward neural networks

Reference 14

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This paper cites Generative adversarial nets.

Dense ReLU Neural Networks for Temporal-spatial Model Generative adversarial nets

Reference 15

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Observation 4662d06e-0a16-46e4-9b80-74d63f87b7d3 · outbound

This paper cites Long short-term memory.

Dense ReLU Neural Networks for Temporal-spatial Model Long short-term memory

Reference 16

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Observation 78a2b5b9-c5c9-461d-9f8f-6e4dd638631f · outbound

This paper cites o rfi, Michael K \.

Dense ReLU Neural Networks for Temporal-spatial Model o rfi, Michael K \

Reference 17

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Observation 6879156a-2977-4d1a-b8d1-da073a8887f3 · outbound

This paper cites Methodology and convergence rates for functional linear regression.

Dense ReLU Neural Networks for Temporal-spatial Model Methodology and convergence rates for functional linear regression

Reference 18

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Observation c4a528dd-7ffd-4736-8cbe-d3de1341ce8b · outbound

This paper cites Nonasymptotic bounds on the l 2 error of neural network regression estimates.

Dense ReLU Neural Networks for Temporal-spatial Model Nonasymptotic bounds on the l 2 error of neural network regression estimates

Reference 19

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This paper cites Kernel methods in machine learning.

Dense ReLU Neural Networks for Temporal-spatial Model Kernel methods in machine learning

Reference 20

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

Dense ReLU Neural Networks for Temporal-spatial Model Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors

Reference 21

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This paper cites Adaptive regression estimation with multilayer feedforward neural networks.

Dense ReLU Neural Networks for Temporal-spatial Model Adaptive regression estimation with multilayer feedforward neural networks

Reference 22

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This paper cites On the rate of convergence of fully connected very deep neural network regression estimates.

Dense ReLU Neural Networks for Temporal-spatial Model On the rate of convergence of fully connected very deep neural network regression estimates

Reference 23

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Dense ReLU Neural Networks for Temporal-spatial Model On the rate of convergence of fully connected deep neural network regression estimates

Reference 24

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Dense ReLU Neural Networks for Temporal-spatial Model Estimation of a regression function on a manifold by fully connected deep neural networks

Reference 25

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Dense ReLU Neural Networks for Temporal-spatial Model Imagenet classification with deep convolutional neural networks

Reference 26

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Dense ReLU Neural Networks for Temporal-spatial Model Imagenet classification with deep convolutional neural networks

Reference 27

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Dense ReLU Neural Networks for Temporal-spatial Model Maximal inequalities and some applications

Reference 28

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This paper cites Statistical methods in spatial epidemiology.

Dense ReLU Neural Networks for Temporal-spatial Model Statistical methods in spatial epidemiology

Reference 29

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Observation 98cc4b40-06f2-4d33-8012-cefaafe74de6 · outbound

This paper cites Theoretical analysis of deep neural networks for temporally dependent observations.

Dense ReLU Neural Networks for Temporal-spatial Model Theoretical analysis of deep neural networks for temporally dependent observations

Reference 30

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This paper cites Convergence rates for single hidden layer feedforward networks.

Dense ReLU Neural Networks for Temporal-spatial Model Convergence rates for single hidden layer feedforward networks

Reference 31

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Dense ReLU Neural Networks for Temporal-spatial Model Rectified linear units improve restricted boltzmann machines

Reference 32

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This paper cites Adaptive Non-Parametric Regression With the $K$-NN Fused Lasso.

Dense ReLU Neural Networks for Temporal-spatial Model Adaptive Non-Parametric Regression With the $K$-NN Fused Lasso

Reference 33

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This paper cites Quantile regression with relu networks: Estimators and minimax rates.

Dense ReLU Neural Networks for Temporal-spatial Model Quantile regression with relu networks: Estimators and minimax rates

Reference 34

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This paper cites Functional data analysis for density functions by transformation to a hilbert space.

Dense ReLU Neural Networks for Temporal-spatial Model Functional data analysis for density functions by transformation to a hilbert space

Reference 35

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This paper cites Improving language understanding by generative pre-training.

Dense ReLU Neural Networks for Temporal-spatial Model Improving language understanding by generative pre-training

Reference 36

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Dense ReLU Neural Networks for Temporal-spatial Model Fast and flexible admm algorithms for trend filtering

Reference 37

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Dense ReLU Neural Networks for Temporal-spatial Model Nonlinear dimensionality reduction by locally linear embedding

Reference 38

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Dense ReLU Neural Networks for Temporal-spatial Model Additive models with trend filtering

Reference 39

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Dense ReLU Neural Networks for Temporal-spatial Model Nonparametric regression using deep neural networks with relu activation function

Reference 40

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Dense ReLU Neural Networks for Temporal-spatial Model Optimal global rates of convergence for nonparametric regression

Reference 41

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Dense ReLU Neural Networks for Temporal-spatial Model Additive regression and other nonparametric models

Reference 42

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Dense ReLU Neural Networks for Temporal-spatial Model The use of polynomial splines and their tensor products in multivariate function estimation

Reference 43

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This paper cites Deepface: Closing the gap to human-level performance in face verification.

Dense ReLU Neural Networks for Temporal-spatial Model Deepface: Closing the gap to human-level performance in face verification

Reference 44

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Dense ReLU Neural Networks for Temporal-spatial Model A global geometric framework for nonlinear dimensionality reduction

Reference 45

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Dense ReLU Neural Networks for Temporal-spatial Model Attention is all you need

Reference 46

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Dense ReLU Neural Networks for Temporal-spatial Model Spatio-temporal statistics with R

Reference 47

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Observation 7a5ee0d1-079d-49d1-ab88-9d4539f10540 · outbound

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Dense ReLU Neural Networks for Temporal-spatial Model Generalized additive models: an introduction with R

Reference 48

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This paper cites The varying driving forces of urban land expansion in china: Insights from a spatial-temporal analysis.

Dense ReLU Neural Networks for Temporal-spatial Model The varying driving forces of urban land expansion in china: Insights from a spatial-temporal analysis

Reference 49

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Pith citing papers

Observation f2b21479-435d-4d9a-9b9c-745ba8afc949 · inbound

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

Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks Dense ReLU Neural Networks for Temporal-spatial Model

Reference 66

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