{"as_of":"2026-08-07T10:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6548227d064ac2e506fd9eae6b92b88386412e4f7bf9f14df485117aab7706c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:35:44.124479Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-06-30T09:54:35.361639Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-05T20:35:44.124479Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.10595","last_updated":"2025-08-14T12:37:22Z","snapshot_observed_at":"2026-08-06T14:20:11.649794Z","submitted_at":"2025-08-14T12:37:22Z","title":"On Spectral Properties of Gradient-based Explanation Methods","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T20:35:44.124479Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2508.10595"},"observation_digest":"sha256:081f840d579dc1a06f4b2132d8e9f01eeb8088a66e8fae464014734a2e1ecbe1","observation_id":"1cd4c059-229e-4b6e-928c-247109297966","resolution":{"observed_at":"2026-08-05T20:35:44.124479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-04T20:20:01.782395Z","title":"& Gross, M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08640","last_updated":"2025-09-10T14:35:24Z","snapshot_observed_at":"2026-08-07T06:43:37.815269Z","submitted_at":"2025-09-10T14:35:24Z","title":"RoentMod: A Synthetic Chest X-Ray Modification Model to Identify and Correct Image Interpretation Model Shortcuts","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T20:20:01.782395Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2509.08640"},"observation_digest":"sha256:f23ee715c6af1946249952330190a4f0df9694f4683555f7dd9407fda812e29d","observation_id":"d1cb5e0c-f7fc-4d71-a5f0-3a2566a5a2fa","resolution":{"observed_at":"2026-08-04T20:20:01.782395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":"1711.06104","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-06-30T09:54:35.361639Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","venue":"cs.LG","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","year":2017},"citing_paper":{"arxiv_id":"2510.22517","last_updated":"2026-06-25T00:44:57Z","snapshot_observed_at":"2026-08-07T09:18:00.644058Z","submitted_at":"2025-10-26T03:50:16Z","title":"Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-18T04:54:33.804678Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2510.22517"},"observation_digest":"sha256:ce418f9879af936c78056acca71292698a235d65f6436ae392832151e5952828","observation_id":"2e05a6a0-82e5-4cb8-9c7e-733edc9be1e5","resolution":{"observed_at":"2026-05-18T04:55:54.311055Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-04T08:07:21.299158Z","title":"& Gross, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.22517","last_updated":"2026-06-25T00:44:57Z","snapshot_observed_at":"2026-08-07T09:18:00.644058Z","submitted_at":"2025-10-26T03:50:16Z","title":"Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T08:07:21.299158Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2510.22517"},"observation_digest":"sha256:cfb0291a02017bd3b8193c90f0e228c979357c8082f90b0354e826ca89bd0493","observation_id":"d0c567a3-6a09-430d-ba34-90ebb5715083","resolution":{"observed_at":"2026-08-04T08:07:21.299158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":"1711.06104","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-06-30T09:54:35.361639Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","venue":"cs.LG","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","year":2017},"citing_paper":{"arxiv_id":"2604.02570","last_updated":"2026-04-02T22:49:57Z","snapshot_observed_at":"2026-07-06T22:51:57.889822Z","submitted_at":"2026-04-02T22:49:57Z","title":"WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T21:05:09.254086Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2604.02570"},"observation_digest":"sha256:c6522ebd93a096766cbb87305c58145fce28bbc2b1b6498095ae0757d762e33f","observation_id":"0279374c-4237-486d-9eea-b9eede67b469","resolution":{"observed_at":"2026-05-13T21:08:18.013467Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":"1711.06104","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-06-30T09:54:35.361639Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","venue":"cs.LG","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","year":2017},"citing_paper":{"arxiv_id":"2605.16905","last_updated":"2026-05-16T09:36:58Z","snapshot_observed_at":"2026-07-06T23:27:57.805018Z","submitted_at":"2026-05-16T09:36:58Z","title":"AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T20:24:19.446451Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2605.16905"},"observation_digest":"sha256:092616de9ee021a0cd8cac310fe2d2e02cb5fb8cdebdc52cffe97d13a068eb8d","observation_id":"a1ce4127-c27c-4014-8385-1f64473f3809","resolution":{"observed_at":"2026-05-19T20:27:54.092397Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":"1711.06104","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-06-30T09:54:35.361639Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","venue":"cs.LG","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","year":2017},"citing_paper":{"arxiv_id":"2606.28391","last_updated":"2026-06-23T17:26:47Z","snapshot_observed_at":"2026-07-07T00:02:29.000757Z","submitted_at":"2026-06-23T17:26:47Z","title":"Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T09:46:17.466028Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2606.28391"},"observation_digest":"sha256:63bed5266240bdce9be9382d60ada036022ccf9b6f9eced28b159608df6d737c","observation_id":"74934315-b9dc-4817-9ef3-6860cb05aa25","resolution":{"observed_at":"2026-06-30T09:54:35.363137Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-01T07:41:35.446900Z","title":"A unified view of gradient-based attribution methods for deep neu- ral networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21381","last_updated":"2026-07-23T14:44:51Z","snapshot_observed_at":"2026-08-04T09:43:41.500487Z","submitted_at":"2026-07-23T14:44:51Z","title":"Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-01T07:41:35.446900Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2607.21381"},"observation_digest":"sha256:aded845ce1bd85de82d28d8a542242654ef9cb7c2e90a2f75cd06e13b359fcf6","observation_id":"88c4b026-b01b-4cb4-8967-57ae964e187a","resolution":{"observed_at":"2026-08-01T07:41:35.446900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-04T01:33:50.219659Z","title":"Asadi, M.; O’Sullivan, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00076","last_updated":"2026-08-04T08:25:13Z","snapshot_observed_at":"2026-08-07T10:11:26.230665Z","submitted_at":"2026-07-29T13:33:11Z","title":"Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-04T01:33:50.219659Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2608.00076"},"observation_digest":"sha256:d61de32adcb97ec796021acc5ab362e4cb0cf251d27e79ec4c1b4b10aa273995","observation_id":"d561605f-d8f7-430d-9a34-ce2e33e59e97","resolution":{"observed_at":"2026-08-04T01:33:50.219659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.06104","snapshot_observed_at":"2026-08-05T04:27:07.839040Z","title":"Asadi, M.; O’Sullivan, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00076","last_updated":"2026-08-04T08:25:13Z","snapshot_observed_at":"2026-08-07T10:11:26.230665Z","submitted_at":"2026-07-29T13:33:11Z","title":"Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-05T04:27:07.839040Z"},"links":{"cited_paper":"/paper/1711.06104","citing_paper":"/paper/2608.00076"},"observation_digest":"sha256:cd56484b6cf8bbb90139c01f32e04bfbb9f3aea904473e3bc50cfe23a35d3ecd","observation_id":"bf3f7145-7f23-4501-b08c-a652474b1488","resolution":{"observed_at":"2026-08-05T04:27:07.839040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1711.06104/citation-record","integrity":"/paper/1711.06104/integrity","json":"/paper/1711.06104/citation-record.json","paper":"/paper/1711.06104"},"outbound":[],"paper":{"arxiv_id":"1711.06104","last_updated":"2018-03-07T10:49:28Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T06:09:50.743518Z","submitted_at":"2017-11-16T14:19:29Z","title":"Towards better understanding of gradient-based attribution methods for Deep Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1711.06104."}