{"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/get-hparams","entry":"get_hparams","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":9,"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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":5},"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":"2406.03184","paper":"/paper/ouroboros3d-image-to-3d-generation-via-3d","title":"Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Costwen/Ouroboros3D","path":"src/utils/config.py","file_url":"https://github.com/Costwen/Ouroboros3D/blob/HEAD/src/utils/config.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":"f5b98a41b3655fc4","mcp_get_code":{"code_sha256":"f5b98a41b3655fc4"}},{"arxiv_id":"2401.06091","paper":"/paper/a-closer-look-at-auroc-and-auprc-under-class","title":"A Closer Look at AUROC and AUPRC under Class Imbalance","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzhang0/auc_bias","path":"experiments.py","file_url":"https://github.com/hzhang0/auc_bias/blob/HEAD/experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"73d0cb5bbc5c4f0c","mcp_get_code":{"code_sha256":"73d0cb5bbc5c4f0c"}},{"arxiv_id":"2210.10769","paper":"/paper/why-did-the-model-fail-attributing-model","title":"\"Why did the Model Fail?\": Attributing Model Performance Changes to Distribution Shifts","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlforhealth/expl_perf_drop","path":"expl_perf_drop/experiments.py","file_url":"https://github.com/mlforhealth/expl_perf_drop/blob/HEAD/expl_perf_drop/experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"73d0cb5bbc5c4f0c","mcp_get_code":{"code_sha256":"73d0cb5bbc5c4f0c"}},{"arxiv_id":"2108.12510","paper":"/paper/pulling-up-by-the-causal-bootstraps-causal","title":"Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing","date":"2021-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MLforHealth/CausalDA","path":"experiments.py","file_url":"https://github.com/MLforHealth/CausalDA/blob/HEAD/experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1333b4e802eba4f0","mcp_get_code":{"code_sha256":"1333b4e802eba4f0"}},{"arxiv_id":"2108.12250","paper":"/paper/a-comparison-of-approaches-to-improve-worst","title":"A comparison of approaches to improve worst-case predictive model performance over patient subpopulations","date":"2021-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"som-shahlab/subpopulation_robustness","path":"group_robustness_fairness/mimic_eicu/experiments.py","file_url":"https://github.com/som-shahlab/subpopulation_robustness/blob/HEAD/group_robustness_fairness/mimic_eicu/experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1333b4e802eba4f0","mcp_get_code":{"code_sha256":"1333b4e802eba4f0"}},{"arxiv_id":"2106.16209","paper":"/paper/s2c2-an-orthogonal-method-for-semi-supervised","title":"A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering","date":"2021-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emprime/dc3","path":"imagenet/training.py","file_url":"https://github.com/emprime/dc3/blob/HEAD/imagenet/training.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":"d418bfeffed76d00","mcp_get_code":{"code_sha256":"d418bfeffed76d00"}},{"arxiv_id":"2002.03079","paper":"/paper/blank-language-models","title":"Blank Language Models","date":"2020-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Varal7/blank_language_model","path":"utils.py","file_url":"https://github.com/Varal7/blank_language_model/blob/HEAD/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":"96be2538380bf7f7","mcp_get_code":{"code_sha256":"96be2538380bf7f7"}},{"arxiv_id":"2002.00434","paper":"/paper/integrating-deep-reinforcement-learning-with","title":"Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving","date":"2020-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ekim-Yurtsever/Hybrid-DeepRL-Automated-Driving","path":"hybrid-rl/sources/common.py","file_url":"https://github.com/Ekim-Yurtsever/Hybrid-DeepRL-Automated-Driving/blob/HEAD/hybrid-rl/sources/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83a20a9bd5e1d806","mcp_get_code":{"code_sha256":"83a20a9bd5e1d806"}},{"arxiv_id":"2001.07685","paper":"/paper/fixmatch-simplifying-semi-supervised-learning","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","date":"2020-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"johnchenresearch/SSL","path":"imagenet/training.py","file_url":"https://github.com/johnchenresearch/SSL/blob/HEAD/imagenet/training.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":"d418bfeffed76d00","mcp_get_code":{"code_sha256":"d418bfeffed76d00"}}]}