{"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/init-group-params","entry":"init_group_params","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":7,"n_papers_ran":0,"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":2,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"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":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/convnext","path":"src/optim.py","file_url":"https://github.com/0jason000/convnext/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/mae_vit","path":"src/optim.py","file_url":"https://github.com/0jason000/mae_vit/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/ghostnet","path":"src/optim.py","file_url":"https://github.com/0jason000/ghostnet/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LKLQQ/LEO","path":"src/trainonestepcell.py","file_url":"https://github.com/LKLQQ/LEO/blob/HEAD/src/trainonestepcell.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":"db5610da74aa6eb4","mcp_get_code":{"code_sha256":"db5610da74aa6eb4"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/DenseNet","path":"src/optim.py","file_url":"https://github.com/0jason000/DenseNet/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}},{"arxiv_id":"1512.00567","paper":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/inception_v3","path":"src/optim.py","file_url":"https://github.com/0jason000/inception_v3/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}},{"arxiv_id":"1409.4842","paper":"/paper/going-deeper-with-convolutions","title":"Going Deeper with Convolutions","date":"2014-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/GoogleNet","path":"src/optim.py","file_url":"https://github.com/0jason000/GoogleNet/blob/HEAD/src/optim.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":"698131638ff8d4ad","mcp_get_code":{"code_sha256":"698131638ff8d4ad"}}]}