{"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/layer-norm-compute-python","entry":"layer_norm_compute_python","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":5,"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":5,"n_places_pointer_only":1,"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":"2005.04732","paper":"/paper/towards-robustifying-nli-models-against","title":"Towards Robustifying NLI Models Against Lexical Dataset Biases","date":"2020-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"owenzx/LexicalDebias-ACL2020","path":"models/hex.py","file_url":"https://github.com/owenzx/LexicalDebias-ACL2020/blob/HEAD/models/hex.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df5f1c26bcb94d35","mcp_get_code":{"code_sha256":"df5f1c26bcb94d35"}},{"arxiv_id":"1909.06356","paper":"/paper/addressing-semantic-drift-in-question","title":"Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering","date":"2019-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhangShiyue/QGforQA","path":"LIB/layers.py","file_url":"https://github.com/ZhangShiyue/QGforQA/blob/HEAD/LIB/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df5f1c26bcb94d35","mcp_get_code":{"code_sha256":"df5f1c26bcb94d35"}},{"arxiv_id":"1607.06450","paper":"/paper/layer-normalization","title":"Layer Normalization","date":"2016-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangyaoyuan/GAN-Simplification","path":"tensor2tensor/common_layers.py","file_url":"https://github.com/zhangyaoyuan/GAN-Simplification/blob/HEAD/tensor2tensor/common_layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80444e734e109863","mcp_get_code":{"code_sha256":"80444e734e109863"}},{"arxiv_id":"1603.01547","paper":"/paper/text-understanding-with-the-attention-sum","title":"Text Understanding with the Attention Sum Reader Network","date":"2016-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"libertatis/mrc-cbt","path":"layers.py","file_url":"https://github.com/libertatis/mrc-cbt/blob/HEAD/layers.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":"80444e734e109863","mcp_get_code":{"code_sha256":"80444e734e109863"}},{"arxiv_id":"1508.06615","paper":"/paper/character-aware-neural-language-models","title":"Character-Aware Neural Language Models","date":"2015-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NLPLearn/QANet","path":"layers.py","file_url":"https://github.com/NLPLearn/QANet/blob/HEAD/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df5f1c26bcb94d35","mcp_get_code":{"code_sha256":"df5f1c26bcb94d35"}}]}