Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1906.10670.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:12.293428Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T17:38:11.296091Z
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 f0873557-80c1-42df-9b0a-7cc2ffa8905b · inbound
On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models cites this paper.
On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:15:12.293428Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c625cc35-5663-4b44-9342-b42f5c613330 · inbound
DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations cites this paper.
DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:23:29.599076Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8e802bc-ed58-4b84-acc1-ca3a1f90915c · inbound
Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability cites this paper.
Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T17:38:11.298201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.