Paper Citation Record · LEDGER

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2504.13412.

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

pith.paper-citation-record.v1
2504.13412 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:15:20.955215Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T01:58:02.062722Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T01:58:28.928528Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 1b4e6c52-8e74-4b61-96d1-23295be41e31 · outbound

This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Optuna: A Next-generation Hyperparameter Optimization Framework

Reference 1

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Observation d84bad0f-f43c-4f19-80da-58630b7a57c9 · outbound

This paper cites Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P

Reference 2

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Observation 8f05e616-1fe5-4847-a1b5-49f13a0bf036 · outbound

This paper cites The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies

Reference 3

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Observation 1a0ae925-a9d2-4690-95ff-e1965495ba86 · outbound

This paper cites Frequency Bias in Neural Networks for Input of Non-Uniform Density.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Frequency Bias in Neural Networks for Input of Non-Uniform Density

Reference 4

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Observation 0ab9360a-0a5a-45df-bcfd-d32ee14da299 · outbound

This paper cites Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

Reference 5

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Observation 8fb2a4aa-827e-4209-8182-22cb373e9623 · outbound

This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 6

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Observation 7419a7ce-bdd5-4f14-b657-4958a9ac9cb6 · outbound

This paper cites ImageNet: A Large-Scale Hierarchical Image Database.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings ImageNet: A Large-Scale Hierarchical Image Database

Reference 7

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Observation b12688ef-e0a8-42eb-bf49-5c2e0844171c · outbound

This paper cites Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

Reference 8

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Observation fa3474aa-702f-49a8-9692-0d09ab2aec7d · outbound

This paper cites Plenoxels: Radiance Fields without Neural Networks.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Plenoxels: Radiance Fields without Neural Networks

Reference 9

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Observation 826b34d7-a261-4769-a74a-5ff425438cd5 · outbound

This paper cites Neural radiosity.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Neural radiosity

Reference 10

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Observation 4c42390b-d3ea-402b-9e87-2ea64c122c67 · outbound

This paper cites Deep ReLU Networks Have Surprisingly Few Activation Patterns.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Deep ReLU Networks Have Surprisingly Few Activation Patterns

Reference 11

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Observation a9fa79f6-651a-4443-9265-622738bf1d5d · outbound

This paper cites Efficient physics-informed neural networks using hash encoding.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Efficient physics-informed neural networks using hash encoding

Reference 12

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Observation 1c88ef98-f5a2-4e65-9e20-deda9d135d95 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 13

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Observation 72fdf508-cd1e-408a-a256-5adbc62fe184 · outbound

This paper cites Stochastic Estimation of the Maximum of a Regression Function.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Stochastic Estimation of the Maximum of a Regression Function

Reference 14

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Observation 4c94ea1d-d6c5-4867-8d5b-488e66e954a9 · outbound

This paper cites Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics

Reference 15

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Observation 8133beb4-0d7d-4fe7-8b13-59e2e82bd60c · outbound

This paper cites Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK

Reference 16

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Observation 32ee540e-76f4-4da0-a13e-126bcbd686a4 · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings DeepXDE: A deep learning library for solving differential equations

Reference 17

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Observation 9705eecc-bfe5-44ed-a434-211bbdf4a446 · outbound

This paper cites Occupancy Networks: Learning 3D Reconstruction in Function Space.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Occupancy Networks: Learning 3D Reconstruction in Function Space

Reference 18

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Observation 7106aac7-318e-4442-bf6c-01d9c65abe7c · outbound

This paper cites NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Reference 19

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Observation 390163f2-6857-445e-b9eb-badca38d3ad1 · outbound

This paper cites Real-time neural radiance caching for path tracing.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Real-time neural radiance caching for path tracing

Reference 20

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This paper cites Instant neural graphics primitives with a multiresolution hash encoding.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Instant neural graphics primitives with a multiresolution hash encoding

Reference 21

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Observation bc95dbf9-9b35-4851-a28f-2b0fd16171f0 · outbound

This paper cites Fast Finite Width Neural Tangent Kernel.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Fast Finite Width Neural Tangent Kernel

Reference 22

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Observation 020beab7-7fe2-428e-8a1a-650e3b1a7769 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 23

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Observation bb4d6f75-1533-4f7e-b0b0-bb8cb7fb585f · outbound

This paper cites Random features for large-scale kernel machines.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Random features for large-scale kernel machines

Reference 25

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Observation 3f2e0cd9-d630-42cb-acbb-82a351148ace · outbound

This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 26

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This paper cites Advances in kernel methods: support vector learning.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Advances in kernel methods: support vector learning

Reference 27

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Observation a6e000ff-8f4c-4b42-b7ef-d92ccc8ed119 · outbound

This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 28

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Observation 5b7baccf-8e96-47a7-9152-0e9b6e151199 · outbound

This paper cites Zippered polygon meshes from Range images.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Zippered polygon meshes from Range images

Reference 29

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Observation b17b9910-d6c2-4042-ada6-aef6e85a706f · outbound

This paper cites Attention Is All You Need.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Attention Is All You Need

Reference 30

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Observation 4b9d5b9d-7dd8-44f9-8643-73dc985f29d7 · outbound

This paper cites Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields

Reference 31

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This paper cites When and why PINNs fail to train: A neural tangent kernel perspective.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings When and why PINNs fail to train: A neural tangent kernel perspective

Reference 32

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Observation ae302d57-6805-4d28-98bb-8adf8f06613c · outbound

This paper cites On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Reference 33

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This paper cites Embedding the Complete Expansion Graph in Books.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Embedding the Complete Expansion Graph in Books

Reference 34

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Observation 5e3e6abb-8531-4b85-ba6c-a5ed1bac6edb · outbound

This paper cites Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung).

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung)

Reference 35

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Observation 503d2e92-04fe-4ab9-8da3-bbeedec66e1b · outbound

This paper cites Kernel Regression.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Kernel Regression

Reference 36

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This paper cites A Fine-Grained Spectral Perspective on Neural Networks.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings A Fine-Grained Spectral Perspective on Neural Networks

Reference 37

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This paper cites pixelNeRF: Neural Radiance Fields from One or Few Images.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings pixelNeRF: Neural Radiance Fields from One or Few Images

Reference 38

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This paper cites NeRF++: Analyzing and Improving Neural Radiance Fields.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings NeRF++: Analyzing and Improving Neural Radiance Fields

Reference 39

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How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings write newline

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This paper cites @esa (Ref.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings @esa (Ref

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T12:15:20.943065Z

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Observation bf0fe5cf-a274-4462-ba1b-aacc80c33f7d · outbound

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How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings Unresolved cited work

Reference 42

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This paper cites !FWPPΝ; OBo Ȝ^ I?j㗼D Mw! B !& dԟ; V p:ڵ z!jT5SO ͖-[ `Æ JW՗ odÆ HV z o΂ HII 0LlݺUYַEppTK ))i[3HnVn݊'`0Lyի/i LxOLz ! B q ٲe ?00@ =G a׮]vΝ;?)Îؿ ?MniB򗿜 d(|KLLdӦM8Nnc=Q.

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings !FWPPΝ; OBo Ȝ^ I?j㗼D Mw! B !& dԟ; V p:ڵ z!jT5SO ͖-[ `Æ JW՗ odÆ HV z o΂ HII 0LlݺUYַEppTK ))i[3HnVn݊'`0Lyի/i LxOLz ! B q ٲe ?00@ =G a׮]vΝ;?)Îؿ ?MniB򗿜 d(|KLLdӦM8Nnc=Q

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T12:15:20.955215Z

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Pith citing papers

Observation 5b90e175-7830-4bfb-9b84-71e75f47749c · inbound

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel cites this paper.

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings

Reference 142

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