{"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/build-lr-scheduler","entry":"build_lr_scheduler","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":13,"n_papers_ran":5,"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":11,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":8},"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":"2606.21447","paper":"/paper/arxiv-2606-21447","title":"Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"98lingchen/MICCAI2026","path":"modules/optimizers.py","file_url":"https://github.com/98lingchen/MICCAI2026/blob/HEAD/modules/optimizers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3cbefcc988ce7b35","mcp_get_code":{"code_sha256":"3cbefcc988ce7b35"}},{"arxiv_id":"2604.12709","paper":"/paper/arxiv-2604-12709","title":"Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"tianweiy/SeqMRI","path":"activemri/baselines/non_rl.py","file_url":"https://github.com/tianweiy/SeqMRI/blob/HEAD/activemri/baselines/non_rl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da4e439ae86acb52","mcp_get_code":{"code_sha256":"da4e439ae86acb52"}},{"arxiv_id":"2602.16590","paper":"/paper/arxiv-2602-16590","title":"A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"SpaceTimeLab/CLIP-MHAdapter","path":"patches/lr_scheduler.py","file_url":"https://github.com/SpaceTimeLab/CLIP-MHAdapter/blob/HEAD/patches/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"45cd333ed4315768","mcp_get_code":{"code_sha256":"45cd333ed4315768"}},{"arxiv_id":"2506.13181","paper":"/paper/align-then-unlearn-embedding-alignment-for","title":"Align-then-Unlearn: Embedding Alignment for LLM Unlearning","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainableml/align-then-unlearn","path":"project/optim.py","file_url":"https://github.com/explainableml/align-then-unlearn/blob/HEAD/project/optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f516f86cb17161c3","mcp_get_code":{"code_sha256":"f516f86cb17161c3"}},{"arxiv_id":"2403.06801","paper":"/paper/ct2rep-automated-radiology-report-generation","title":"CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibrahimethemhamamci/ct2rep","path":"CT2Rep/modules/optimizers.py","file_url":"https://github.com/ibrahimethemhamamci/ct2rep/blob/HEAD/CT2Rep/modules/optimizers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3cbefcc988ce7b35","mcp_get_code":{"code_sha256":"3cbefcc988ce7b35"}},{"arxiv_id":"2311.05729","paper":"/paper/gipcol-graph-injected-soft-prompting-for","title":"GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlr/gipcol","path":"Models/CoopModel/Scheduler/lr_scheduler.py","file_url":"https://github.com/hlr/gipcol/blob/HEAD/Models/CoopModel/Scheduler/lr_scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a23cf5bf1e4b1696","mcp_get_code":{"code_sha256":"a23cf5bf1e4b1696"}},{"arxiv_id":"2209.05479","paper":"/paper/leveraging-language-foundation-models-for","title":"Leveraging Language Foundation Models for Human Mobility Forecasting","date":"2022-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cruiseresearchgroup/AuxMobLCast","path":"finetune.py","file_url":"https://github.com/cruiseresearchgroup/AuxMobLCast/blob/HEAD/finetune.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0247b46df4706ea5","mcp_get_code":{"code_sha256":"0247b46df4706ea5"}},{"arxiv_id":"2106.07802","paper":"/paper/geomol-torsional-geometric-generation-of","title":"GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PattanaikL/GeoMol","path":"model/training.py","file_url":"https://github.com/PattanaikL/GeoMol/blob/HEAD/model/training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f3b6b2dc21ade673","mcp_get_code":{"code_sha256":"f3b6b2dc21ade673"}},{"arxiv_id":"2103.15808","paper":"/paper/cvt-introducing-convolutions-to-vision","title":"CvT: Introducing Convolutions to Vision Transformers","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/CvT","path":"lib/scheduler/build.py","file_url":"https://github.com/microsoft/CvT/blob/HEAD/lib/scheduler/build.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"60eb73feb15fe78a","mcp_get_code":{"code_sha256":"60eb73feb15fe78a"}},{"arxiv_id":"2004.13922","paper":"/paper/revisiting-pre-trained-models-for-chinese","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","date":"2020-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shibing624/pycorrector","path":"pycorrector/macbert/macbert4csc.py","file_url":"https://github.com/shibing624/pycorrector/blob/HEAD/pycorrector/macbert/macbert4csc.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":"699577a393a1a36e","mcp_get_code":{"code_sha256":"699577a393a1a36e"}},{"arxiv_id":"1805.09501","paper":"/paper/autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2han9x1a0release/RLCC","path":"openunreid/core/solvers/lr_scheduler.py","file_url":"https://github.com/2han9x1a0release/RLCC/blob/HEAD/openunreid/core/solvers/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3db04fbfca62bfd0","mcp_get_code":{"code_sha256":"3db04fbfca62bfd0"}},{"arxiv_id":"Wang_Language-Driven_Multi-Label_Zero-Shot_Learning_with_Semantic_Granularity_ICCV_2025_paper","paper":null,"title":"arXiv:Wang_Language-Driven_Multi-Label_Zero-Shot_Learning_with_Semantic_Granularity_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wangshouwen/RCNn","path":"utils/lr_scheduler.py","file_url":"https://github.com/wangshouwen/RCNn/blob/HEAD/utils/lr_scheduler.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":"f819452abc45fa07","mcp_get_code":{"code_sha256":"f819452abc45fa07"}},{"arxiv_id":"2021.acl-long.459","paper":null,"title":"arXiv:2021.acl-long.459","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cuhksz-nlp/R2GenCMN","path":"modules/optimizers.py","file_url":"https://github.com/cuhksz-nlp/R2GenCMN/blob/HEAD/modules/optimizers.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":"3cbefcc988ce7b35","mcp_get_code":{"code_sha256":"3cbefcc988ce7b35"}}]}