{"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/l1-norm","entry":"L1_norm","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":4,"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":4,"n_samples_ran":3,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":0,"unverified":1},"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":"2404.08406","paper":"/paper/mambadfuse-a-mamba-based-dual-phase-model-for","title":"MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion","date":"2024-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lizhe1228/MambaDFuse","path":"models/network.py","file_url":"https://github.com/Lizhe1228/MambaDFuse/blob/HEAD/models/network.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"610c7dc363563f5b","mcp_get_code":{"code_sha256":"610c7dc363563f5b"}},{"arxiv_id":"2312.01260","paper":"/paper/rethinking-pgd-attack-is-sign-function","title":"Rethinking PGD Attack: Is Sign Function Necessary?","date":"2023-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junjieyang97/rgd","path":"autoattack/other_utils.py","file_url":"https://github.com/junjieyang97/rgd/blob/HEAD/autoattack/other_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"LGPL-3.0","inline_ok":false,"code_sha256_prefix":"edab3f9c146ad457","mcp_get_code":{"code_sha256":"edab3f9c146ad457"}},{"arxiv_id":"2203.15794","paper":"/paper/chex-channel-exploration-for-cnn-model","title":"CHEX: CHannel EXploration for CNN Model Compression","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zejiangh/Filter-GaP","path":"CLS/image_classification/prune_utils.py","file_url":"https://github.com/zejiangh/Filter-GaP/blob/HEAD/CLS/image_classification/prune_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ecfd1ac200576251","mcp_get_code":{"code_sha256":"ecfd1ac200576251"}},{"arxiv_id":"2103.01208","paper":"/paper/mind-the-box-l-1-apgd-for-sparse-adversarial","title":"Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers","date":"2021-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/auto-attack","path":"autoattack/autopgd_base.py","file_url":"https://github.com/fra31/auto-attack/blob/HEAD/autoattack/autopgd_base.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edab3f9c146ad457","mcp_get_code":{"code_sha256":"edab3f9c146ad457"}},{"arxiv_id":"2001.02589","paper":"/paper/machine-learning-enables-completely-automatic","title":"Machine learning enables completely automatic tuning of a quantum device faster than human experts","date":"2020-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oxquantum-repo/Tuning_old","path":"config_model.py","file_url":"https://github.com/oxquantum-repo/Tuning_old/blob/HEAD/config_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae1213c30b4f0ddd","mcp_get_code":{"code_sha256":"ae1213c30b4f0ddd"}}]}