{"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/adjust-lr","entry":"adjust_lr","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":16,"n_papers_ran":12,"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":6,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":16,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"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":"2506.21866","paper":null,"title":"arXiv:2506.21866","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"CSYSI/DPU-Former","path":"DPU-Former_25_IJCAI/utils/utils.py","file_url":"https://github.com/CSYSI/DPU-Former/blob/HEAD/DPU-Former_25_IJCAI/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1a4b7ff9d3c48bd7","mcp_get_code":{"code_sha256":"1a4b7ff9d3c48bd7"}},{"arxiv_id":"2403.07591","paper":"/paper/robustifying-and-boosting-training-free","title":"Robustifying and Boosting Training-Free Neural Architecture Search","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzf1174/RoBoT","path":"darts_space/train_imagenet.py","file_url":"https://github.com/hzf1174/RoBoT/blob/HEAD/darts_space/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e127535d5e962cac","mcp_get_code":{"code_sha256":"e127535d5e962cac"}},{"arxiv_id":"2307.07205","paper":"/paper/multimodal-motion-conditioned-diffusion-model","title":"Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection","date":"2023-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aleflabo/MoCoDAD","path":"utils/model_utils.py","file_url":"https://github.com/aleflabo/MoCoDAD/blob/HEAD/utils/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"935b328e2a4ca4dd","mcp_get_code":{"code_sha256":"935b328e2a4ca4dd"}},{"arxiv_id":"2301.09489","paper":"/paper/contracting-skeletal-kinematic-embeddings-for","title":"Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection","date":"2023-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aleflabo/COSKAD","path":"utils/model_utils.py","file_url":"https://github.com/aleflabo/COSKAD/blob/HEAD/utils/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"935b328e2a4ca4dd","mcp_get_code":{"code_sha256":"935b328e2a4ca4dd"}},{"arxiv_id":"2203.14291","paper":"/paper/video-polyp-segmentation-a-deep-learning","title":"Video Polyp Segmentation: A Deep Learning Perspective","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GewelsJI/PNS-Net","path":"utils/utils.py","file_url":"https://github.com/GewelsJI/PNS-Net/blob/HEAD/utils/utils.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":"1a4b7ff9d3c48bd7","mcp_get_code":{"code_sha256":"1a4b7ff9d3c48bd7"}},{"arxiv_id":"2108.08165","paper":"/paper/generalized-and-incremental-few-shot-learning","title":"Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting","date":"2021-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"annusha/LCwoF","path":"mini_imgnet/episode_training_clean.py","file_url":"https://github.com/annusha/LCwoF/blob/HEAD/mini_imgnet/episode_training_clean.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db8232296a32d4c7","mcp_get_code":{"code_sha256":"db8232296a32d4c7"}},{"arxiv_id":"2105.15010","paper":"/paper/querynet-an-efficient-attack-framework-with","title":"Query Attack by Multi-Identity Surrogates","date":"2021-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenchen1998/querynet","path":"PCDARTS/train_imagenet.py","file_url":"https://github.com/allenchen1998/querynet/blob/HEAD/PCDARTS/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"2007.02713","paper":"/paper/bbs-net-rgb-d-salient-object-detection-with-a","title":"Bifurcated backbone strategy for RGB-D salient object detection","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DengPingFan/BBS-Net","path":"utils.py","file_url":"https://github.com/DengPingFan/BBS-Net/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a4b7ff9d3c48bd7","mcp_get_code":{"code_sha256":"1a4b7ff9d3c48bd7"}},{"arxiv_id":"2006.16537","paper":"/paper/theory-inspired-path-regularized-differential","title":"Theory-Inspired Path-Regularized Differential Network Architecture Search","date":"2020-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/PR-DARTS","path":"PRDARTS_eval/train_imagenet.py","file_url":"https://github.com/salesforce/PR-DARTS/blob/HEAD/PRDARTS_eval/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"1912.10952","paper":"/paper/progressive-darts-bridging-the-optimization","title":"Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild","date":"2019-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxin061/pdarts","path":"train_imagenet.py","file_url":"https://github.com/chenxin061/pdarts/blob/HEAD/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"1912.00195","paper":"/paper/sgas-sequential-greedy-architecture-search","title":"SGAS: Sequential Greedy Architecture Search","date":"2019-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lightaime/sgas","path":"cnn/train_imagenet.py","file_url":"https://github.com/lightaime/sgas/blob/HEAD/cnn/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"1906.07528","paper":"/paper/prune-and-replace-nas","title":"Prune and Replace NAS","date":"2019-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cogsys-tuebingen/prdarts","path":"train_imagenet.py","file_url":"https://github.com/cogsys-tuebingen/prdarts/blob/HEAD/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"1904.12760","paper":"/paper/progressive-differentiable-architecture","title":"Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation","date":"2019-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"1806.09055","paper":"/paper/darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DoctorLiQ/AutoML","path":"cnn/train_imagenet.py","file_url":"https://github.com/DoctorLiQ/AutoML/blob/HEAD/cnn/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"aaai_26076","paper":null,"title":"arXiv:aaai_26076","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ShunLu91/PINAT","path":"darts/retrain_imagenet.py","file_url":"https://github.com/ShunLu91/PINAT/blob/HEAD/darts/retrain_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7ef53e9c5847e3e","mcp_get_code":{"code_sha256":"e7ef53e9c5847e3e"}},{"arxiv_id":"aaai_25958","paper":null,"title":"arXiv:aaai_25958","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tsingqguo/bgmix","path":"manual_seed.py","file_url":"https://github.com/tsingqguo/bgmix/blob/HEAD/manual_seed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e00e769f1d037c0","mcp_get_code":{"code_sha256":"4e00e769f1d037c0"}}]}