{"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/convert-to-onehot","entry":"convert_to_onehot","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+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":5,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":4},"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":"2305.10564","paper":"/paper/counterfactually-comparing-abstaining-1","title":"Counterfactually Comparing Abstaining Classifiers","date":"2023-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjchoe/ComparingAbstainingClassifiers","path":"comparecast_causal/utils.py","file_url":"https://github.com/yjchoe/ComparingAbstainingClassifiers/blob/HEAD/comparecast_causal/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d24633be058fec72","mcp_get_code":{"code_sha256":"d24633be058fec72"}},{"arxiv_id":"2008.05975","paper":"/paper/deep-learning-to-quantify-pulmonary-edema-in","title":"Deep Learning to Quantify Pulmonary Edema in Chest Radiographs","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RayRuizhiLiao/resnet_chestxray","path":"resnet_chestxray/model_utils.py","file_url":"https://github.com/RayRuizhiLiao/resnet_chestxray/blob/HEAD/resnet_chestxray/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"318cc2b5cc8a9ccd","mcp_get_code":{"code_sha256":"318cc2b5cc8a9ccd"}},{"arxiv_id":"1906.06148","paper":"/paper/a-partially-reversible-u-net-for-memory","title":"A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RobinBruegger/PartiallyReversibleUnet","path":"dataProcessing/utils.py","file_url":"https://github.com/RobinBruegger/PartiallyReversibleUnet/blob/HEAD/dataProcessing/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"655b6c41d66f6e08","mcp_get_code":{"code_sha256":"655b6c41d66f6e08"}},{"arxiv_id":"1902.06673","paper":"/paper/fake-news-detection-on-social-media-using","title":"Fake News Detection on Social Media using Geometric Deep Learning","date":"2019-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"npurg/faknow","path":"faknow/model/content_based/m3fend.py","file_url":"https://github.com/npurg/faknow/blob/HEAD/faknow/model/content_based/m3fend.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f32d51fc968cd31","mcp_get_code":{"code_sha256":"2f32d51fc968cd31"}},{"arxiv_id":"1805.06605","paper":"/paper/defense-gan-protecting-classifiers-against","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","date":"2018-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kabkabm/defensegan","path":"blackbox.py","file_url":"https://github.com/kabkabm/defensegan/blob/HEAD/blackbox.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"30d94d11f9899a82","mcp_get_code":{"code_sha256":"30d94d11f9899a82"}}]}