Paper Citation Record · LEDGER

Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.16483.

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

pith.paper-citation-record.v1
2312.16483 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:40:45.487264Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T18:34:59.815083Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 ac232a32-e60c-4cc6-95fb-2f5cddae0da7 · inbound

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants cites this paper.

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T04:40:45.487264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4457620-0726-4016-a3f1-608a7dc23147 · inbound

Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness cites this paper.

Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T23:33:58.411144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97953d40-1baf-4f64-aacd-22c268db6da0 · inbound

Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization cites this paper.

Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:34:59.816650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1cc62302-0fde-46f7-a9e3-bd9b5bdd87fe · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

Reference 127

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:31:14.049036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f69c8a63-ddbf-47d7-9f7c-9513d1b5c128 · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.104487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.