Paper Citation Record · LEDGER

A Benchmark for Interpretability Methods in Deep Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1806.10758.

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

pith.paper-citation-record.v1
1806.10758 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:42:30.126213Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

382
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 226948c1-8f30-4d05-a399-2cd26a35f07b · inbound

New Faithfulness-Centric Interpretability Paradigms for Natural Language Processing cites this paper.

New Faithfulness-Centric Interpretability Paradigms for Natural Language Processing A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T11:42:30.126213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a15ce39-0f51-4001-906f-cd4cb8c786ce · inbound

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks cites this paper.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T12:14:31.659551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14306636-8bac-4328-b088-31ff7f53f951 · inbound

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations cites this paper.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T20:29:39.545287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee48c4d-32e7-4382-80c3-1f51dfa4e0d1 · inbound

On Spectral Properties of Gradient-based Explanation Methods cites this paper.

On Spectral Properties of Gradient-based Explanation Methods A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T20:35:46.382385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fd54bfb-c744-41b7-84e3-4687b3a10758 · inbound

Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity cites this paper.

Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:11:39.132233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee398078-55e7-487e-9bbd-1362f928d687 · inbound

From Features to Actions: Explainability in Traditional and Agentic AI Systems cites this paper.

From Features to Actions: Explainability in Traditional and Agentic AI Systems A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T03:50:15.726973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56980aef-5f9e-4850-b55b-70f435a36c00 · inbound

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification cites this paper.

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:25:46.633942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 543328fa-3490-4579-ae11-531a472fbd0c · inbound

AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps cites this paper.

AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:27:54.129510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4f44b6ac-c385-4307-9f59-0311823cb187 · inbound

Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection cites this paper.

Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T13:43:28.433828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b046f16c-1dbf-440a-aae5-c3a7e1994e89 · inbound

Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations cites this paper.

Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:54:35.365695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.