{"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/calc-auc","entry":"calc_auc","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":3,"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":5,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":2,"unverified":2},"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":"2410.02604","paper":"/paper/long-sequence-recommendation-models-need","title":"Long-Sequence Recommendation Models Need Decoupled Embeddings","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/DARE","path":"utils.py","file_url":"https://github.com/thuml/DARE/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7873f0e1fa137cda","mcp_get_code":{"code_sha256":"7873f0e1fa137cda"}},{"arxiv_id":"2308.08487","paper":"/paper/temporal-interest-network-for-click-through","title":"Temporal Interest Network for User Response Prediction","date":"2023-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouxy1003/tin","path":"model/bert4rec/train_bert4rec.py","file_url":"https://github.com/zhouxy1003/tin/blob/HEAD/model/bert4rec/train_bert4rec.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b8f6d1bfbb4bc60","mcp_get_code":{"code_sha256":"2b8f6d1bfbb4bc60"}},{"arxiv_id":"2203.08586","paper":"/paper/deep-vanishing-point-detection-geometric","title":"Deep vanishing point detection: Geometric priors make dataset variations vanish","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanconglin/vanishingpoint_houghtransform_gaussiansphere","path":"cluster_nyu.py","file_url":"https://github.com/yanconglin/vanishingpoint_houghtransform_gaussiansphere/blob/HEAD/cluster_nyu.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52bdb37b8c80883e","mcp_get_code":{"code_sha256":"52bdb37b8c80883e"}},{"arxiv_id":"2007.05201","paper":"/paper/rose-a-retinal-oct-angiography-vessel","title":"ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model","date":"2020-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network","path":"code/OCTA-Net/evaluation.py","file_url":"https://github.com/iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network/blob/HEAD/code/OCTA-Net/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f1a1dc397cac1ca3","mcp_get_code":{"code_sha256":"f1a1dc397cac1ca3"}},{"arxiv_id":"2006.06979","paper":"/paper/non-negative-bregman-divergence-minimization","title":"Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MasaKat0/D3RE","path":"CovariateShift/covariate_shift_combinatorial.py","file_url":"https://github.com/MasaKat0/D3RE/blob/HEAD/CovariateShift/covariate_shift_combinatorial.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b8cf683e0270415a","mcp_get_code":{"code_sha256":"b8cf683e0270415a"}}]}