{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/integrated-gradients","entry":"integrated_gradients","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":1,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":10,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":9},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2602.02215","paper":"/paper/arxiv-2602-02215","title":"Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"semueller/stobb_cobot","path":"lxg/attribution.py","file_url":"https://github.com/semueller/stobb_cobot/blob/HEAD/lxg/attribution.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"667758f31b1b6656","mcp_get_code":{"code_sha256":"667758f31b1b6656"}},{"arxiv_id":"2601.03429","paper":"/paper/arxiv-2601-03429","title":"DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"um-dsp/DeepLeak","path":"xai_methods/captum_wrappers.py","file_url":"https://github.com/um-dsp/DeepLeak/blob/HEAD/xai_methods/captum_wrappers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"754864e120b68419","mcp_get_code":{"code_sha256":"754864e120b68419"}},{"arxiv_id":"2311.14029","paper":"/paper/understanding-the-vulnerability-of-clip-to","title":"Understanding the Vulnerability of CLIP to Image Compression","date":"2023-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CangxiongChen/understanding_CLIP_vulnerability","path":"compute_IG.py","file_url":"https://github.com/CangxiongChen/understanding_CLIP_vulnerability/blob/HEAD/compute_IG.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6224194f56968bca","mcp_get_code":{"code_sha256":"6224194f56968bca"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ankurtaly/Attributions","path":"IntegratedGradients/integrated_gradients.py","file_url":"https://github.com/ankurtaly/Attributions/blob/HEAD/IntegratedGradients/integrated_gradients.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f36f0780dc332c6d","mcp_get_code":{"code_sha256":"f36f0780dc332c6d"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marnifora/magisterka","path":"bin/integrated_gradients.py","file_url":"https://github.com/marnifora/magisterka/blob/HEAD/bin/integrated_gradients.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f457ae7012a204f","mcp_get_code":{"code_sha256":"6f457ae7012a204f"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2","path":"code/IntegratedGradients.py","file_url":"https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2/blob/HEAD/code/IntegratedGradients.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"b1ea75b6852183e8","mcp_get_code":{"code_sha256":"b1ea75b6852183e8"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TianhongDai/integrated-gradient-pytorch","path":"integrated_gradients.py","file_url":"https://github.com/TianhongDai/integrated-gradient-pytorch/blob/HEAD/integrated_gradients.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"04f60f57d1b6028e","mcp_get_code":{"code_sha256":"04f60f57d1b6028e"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garygsw/smooth-taylor","path":"attribution/analyzer.py","file_url":"https://github.com/garygsw/smooth-taylor/blob/HEAD/attribution/analyzer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"825842184e64aa86","mcp_get_code":{"code_sha256":"825842184e64aa86"}},{"arxiv_id":"1703.01365","paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pamflecista/Magisterka","path":"bin/integrated_gradients.py","file_url":"https://github.com/pamflecista/Magisterka/blob/HEAD/bin/integrated_gradients.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c69ac1fe40cb16f0","mcp_get_code":{"code_sha256":"c69ac1fe40cb16f0"}},{"arxiv_id":"2023.findings-acl.611","paper":null,"title":"arXiv:2023.findings-acl.611","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/RobTest","path":"src/AttackMethod/ModelBased/searching.py","file_url":"https://github.com/thunlp/RobTest/blob/HEAD/src/AttackMethod/ModelBased/searching.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cb61314ec66f69d","mcp_get_code":{"code_sha256":"0cb61314ec66f69d"}}]}