Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:40:45.487264Z
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
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.