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Paper Citation Record · LEDGER

Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.10766.

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

pith.paper-citation-record.v1
2310.10766 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:14:01.608610Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:18:54.887507Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 78fc962b-553b-4a00-ad9f-2a7028babc06 · inbound

An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks cites this paper.

An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T18:37:42.761827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9a23f61b-9dd4-44a7-bbf4-0af64e9d28d6 · inbound

Learn Singularly Perturbed Solutions via Homotopy Dynamics cites this paper.

Learn Singularly Perturbed Solutions via Homotopy Dynamics Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-09T18:57:13.668386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b3fdfc3b-f258-4524-b92d-3ba165bf2a9d · inbound

$\mathcal{C}^1$-approximation with rational functions and rational neural networks cites this paper.

$\mathcal{C}^1$-approximation with rational functions and rational neural networks Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-05T15:44:54.833793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a38592b0-8914-421d-84ac-ea83deff2bba · inbound

Continuous Data Assimilation with Learned Surrogate Dynamics cites this paper.

Continuous Data Assimilation with Learned Surrogate Dynamics Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-06-28T20:32:37.972575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-28T18:32:59.958496Z digest=sha256:f1a728e4b7a0a2f7b780599b06762283595c5763f40bf4b475dd37e0a955512e

Observation 641397af-b429-4d16-837e-9c3caf62ab2a · inbound

Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces cites this paper.

Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-07-03T19:18:54.888907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T01:55:21.797845Z digest=sha256:836bae92084ab936f6a2c4c18640d4894c614048060c4dda7fc5eaafe9cc8d1a

Observation 35b66c45-bdae-49cc-9930-920cc05a868b · inbound

Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View cites this paper.

Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-15T15:14:01.608610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:14:01.608610Z digest=sha256:f161607d4b53819f6cd066f2399b73516e704a61b658dd226a5c9766990325ce