{"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-std-opt","entry":"get_std_opt","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":6,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2402.12269","paper":"/paper/end-to-end-supervised-prediction-of-arbitrary","title":"Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KrzakalaPaul/Any2Graph","path":"Any2Graph/utils.py","file_url":"https://github.com/KrzakalaPaul/Any2Graph/blob/HEAD/Any2Graph/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"186a7a26fda1c0c0","mcp_get_code":{"code_sha256":"186a7a26fda1c0c0"}},{"arxiv_id":"2104.09715","paper":"/paper/adaspeech-2-adaptive-text-to-speech-with","title":"AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rishikksh20/AdaSpeech2","path":"core/optimizer.py","file_url":"https://github.com/rishikksh20/AdaSpeech2/blob/HEAD/core/optimizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09d756d182147053","mcp_get_code":{"code_sha256":"09d756d182147053"}},{"arxiv_id":"2006.09286","paper":"/paper/on-the-computational-power-of-transformers","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satwik77/Transformer-Computation-Analysis","path":"Transformer/src/utils/helper.py","file_url":"https://github.com/satwik77/Transformer-Computation-Analysis/blob/HEAD/Transformer/src/utils/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c930b9e88eed3346","mcp_get_code":{"code_sha256":"c930b9e88eed3346"}},{"arxiv_id":"2006.04558","paper":"/paper/fastspeech-2-fast-and-high-quality-end-to-end","title":"FastSpeech 2: Fast and High-Quality End-to-End Text to Speech","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rishikksh20/FastSpeech2","path":"core/optimizer.py","file_url":"https://github.com/rishikksh20/FastSpeech2/blob/HEAD/core/optimizer.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":"09d756d182147053","mcp_get_code":{"code_sha256":"09d756d182147053"}},{"arxiv_id":"1910.06188","paper":"/paper/q8bert-quantized-8bit-bert","title":"Q8BERT: Quantized 8Bit BERT","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iabd/QuantizedNMT","path":"src/noamOpt.py","file_url":"https://github.com/iabd/QuantizedNMT/blob/HEAD/src/noamOpt.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":"b08eb2ca2806310a","mcp_get_code":{"code_sha256":"b08eb2ca2806310a"}},{"arxiv_id":"1909.06639","paper":"/paper/tree-transformer-integrating-tree-structures","title":"Tree Transformer: Integrating Tree Structures into Self-Attention","date":"2019-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itzpankajpanwar/Tree-transform","path":"modules.py","file_url":"https://github.com/itzpankajpanwar/Tree-transform/blob/HEAD/modules.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":"009d3f80a8ce3436","mcp_get_code":{"code_sha256":"009d3f80a8ce3436"}}]}