{"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/find-best-lrwd","entry":"find_best_lrwd","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":6,"n_papers_ran":2,"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":0,"n_places":13,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"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":"2411.01327","paper":"/paper/visual-fourier-prompt-tuning","title":"Visual Fourier Prompt Tuning","date":"2024-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"runtsang/VFPT","path":"tune_fgvc_final.py","file_url":"https://github.com/runtsang/VFPT/blob/HEAD/tune_fgvc_final.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}},{"arxiv_id":"2401.12902","paper":"/paper/facing-the-elephant-in-the-room-visual-prompt","title":"Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengHan111/VPT-or-FT","path":"five_runs_fgvc.py","file_url":"https://github.com/ChengHan111/VPT-or-FT/blob/HEAD/five_runs_fgvc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f24fe10a4c3795a2","mcp_get_code":{"code_sha256":"f24fe10a4c3795a2"}},{"arxiv_id":"2401.12902","paper":"/paper/facing-the-elephant-in-the-room-visual-prompt","title":"Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengHan111/VPT-or-FT","path":"tune_fgvc_fiveRuns.py","file_url":"https://github.com/ChengHan111/VPT-or-FT/blob/HEAD/tune_fgvc_fiveRuns.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0f806382f4ff9673","mcp_get_code":{"code_sha256":"0f806382f4ff9673"}},{"arxiv_id":"2401.12902","paper":"/paper/facing-the-elephant-in-the-room-visual-prompt","title":"Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengHan111/VPT-or-FT","path":"tune_vtab_AS.py","file_url":"https://github.com/ChengHan111/VPT-or-FT/blob/HEAD/tune_vtab_AS.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"96cd94d7836ffe52","mcp_get_code":{"code_sha256":"96cd94d7836ffe52"}},{"arxiv_id":"2401.12902","paper":"/paper/facing-the-elephant-in-the-room-visual-prompt","title":"Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengHan111/VPT-or-FT","path":"tune_vtab_PruningRewind_CVK_imagenet1k.py","file_url":"https://github.com/ChengHan111/VPT-or-FT/blob/HEAD/tune_vtab_PruningRewind_CVK_imagenet1k.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}},{"arxiv_id":"2312.10376","paper":"/paper/sa-2-vp-spatially-aligned-and-adapted-visual","title":"SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt","date":"2023-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tommy-xq/sa2vp","path":"vpt_main/tune_vtab.py","file_url":"https://github.com/tommy-xq/sa2vp/blob/HEAD/vpt_main/tune_vtab.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}},{"arxiv_id":"2307.13770","paper":"/paper/e-2vpt-an-effective-and-efficient-approach","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","date":"2023-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenghan111/e2vpt","path":"five_runs_fgvc.py","file_url":"https://github.com/chenghan111/e2vpt/blob/HEAD/five_runs_fgvc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f24fe10a4c3795a2","mcp_get_code":{"code_sha256":"f24fe10a4c3795a2"}},{"arxiv_id":"2307.13770","paper":"/paper/e-2vpt-an-effective-and-efficient-approach","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","date":"2023-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenghan111/e2vpt","path":"tune_fgvc_fiveRuns.py","file_url":"https://github.com/chenghan111/e2vpt/blob/HEAD/tune_fgvc_fiveRuns.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0f806382f4ff9673","mcp_get_code":{"code_sha256":"0f806382f4ff9673"}},{"arxiv_id":"2307.13770","paper":"/paper/e-2vpt-an-effective-and-efficient-approach","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","date":"2023-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenghan111/e2vpt","path":"tune_vtab_AS.py","file_url":"https://github.com/chenghan111/e2vpt/blob/HEAD/tune_vtab_AS.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"96cd94d7836ffe52","mcp_get_code":{"code_sha256":"96cd94d7836ffe52"}},{"arxiv_id":"2307.13770","paper":"/paper/e-2vpt-an-effective-and-efficient-approach","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","date":"2023-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenghan111/e2vpt","path":"tune_vtab_PruningRewind_CVK_imagenet1k.py","file_url":"https://github.com/chenghan111/e2vpt/blob/HEAD/tune_vtab_PruningRewind_CVK_imagenet1k.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}},{"arxiv_id":"2212.03220","paper":"/paper/visual-query-tuning-towards-effective-usage","title":"Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andytu28/VQT","path":"tune_vtab.py","file_url":"https://github.com/andytu28/VQT/blob/HEAD/tune_vtab.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":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}},{"arxiv_id":"2212.03220","paper":"/paper/visual-query-tuning-towards-effective-usage","title":"Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andytu28/VQT","path":"head2toe_sparsity_train.py","file_url":"https://github.com/andytu28/VQT/blob/HEAD/head2toe_sparsity_train.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":"51201372d14e1a56","mcp_get_code":{"code_sha256":"51201372d14e1a56"}},{"arxiv_id":"aaai_28243","paper":null,"title":"arXiv:aaai_28243","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tommy-xq/SA2VP","path":"vpt_main/tune_vtab.py","file_url":"https://github.com/tommy-xq/SA2VP/blob/HEAD/vpt_main/tune_vtab.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e009472c0d2fa50","mcp_get_code":{"code_sha256":"3e009472c0d2fa50"}}]}