{"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/nanmean","entry":"nanmean","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":4,"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":2,"n_samples_fingerprinted":2,"n_places":10,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"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":"2405.20690","paper":"/paper/unleashing-the-potential-of-diffusion-models","title":"Unleashing the Potential of Diffusion Models for Incomplete Data Imputation","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hezgit/TDM","path":"utils.py","file_url":"https://github.com/hezgit/TDM/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"bbbb6c22bd6a9a4a","mcp_get_code":{"code_sha256":"bbbb6c22bd6a9a4a"}},{"arxiv_id":"2404.05163","paper":"/paper/semantic-flow-learning-semantic-field-of","title":"Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfr/Semantic-Flow","path":"train/evaluation_for_semantic_logs.py","file_url":"https://github.com/tianfr/Semantic-Flow/blob/HEAD/train/evaluation_for_semantic_logs.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"038ae280297cca06","mcp_get_code":{"code_sha256":"038ae280297cca06"}},{"arxiv_id":"2309.16779","paper":"/paper/intriguing-properties-of-generative","title":"Intriguing properties of generative classifiers","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SamsungSAILMontreal/ForestDiffusion","path":"utils.py","file_url":"https://github.com/SamsungSAILMontreal/ForestDiffusion/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbbb6c22bd6a9a4a","mcp_get_code":{"code_sha256":"bbbb6c22bd6a9a4a"}},{"arxiv_id":"2302.10363","paper":"/paper/transformed-distribution-matching-for-missing","title":"Transformed Distribution Matching for Missing Value Imputation","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hezgit/tdm","path":"tdm.py","file_url":"https://github.com/hezgit/tdm/blob/HEAD/tdm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"bbbb6c22bd6a9a4a","mcp_get_code":{"code_sha256":"bbbb6c22bd6a9a4a"}},{"arxiv_id":"2212.07328","paper":"/paper/modeling-multimodal-aleatoric-uncertainty-in","title":"Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts","date":"2022-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"5574012ba4af04ff","mcp_get_code":{"code_sha256":"5574012ba4af04ff"}},{"arxiv_id":"2205.13760","paper":"/paper/tranception-protein-fitness-prediction-with","title":"Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval","date":"2022-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OATML-Markslab/Tranception","path":"tranception/model_pytorch.py","file_url":"https://github.com/OATML-Markslab/Tranception/blob/HEAD/tranception/model_pytorch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5574012ba4af04ff","mcp_get_code":{"code_sha256":"5574012ba4af04ff"}},{"arxiv_id":"2106.02584","paper":"/paper/self-attention-between-datapoints-going","title":"Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oatml-markslab/proteinnpt","path":"proteinnpt/utils/tranception/model_pytorch.py","file_url":"https://github.com/oatml-markslab/proteinnpt/blob/HEAD/proteinnpt/utils/tranception/model_pytorch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5574012ba4af04ff","mcp_get_code":{"code_sha256":"5574012ba4af04ff"}},{"arxiv_id":"1910.13675","paper":"/paper/form2fit-learning-shape-priors-for","title":"Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly","date":"2019-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevinzakka/form2fit","path":"form2fit/code/ml/losses.py","file_url":"https://github.com/kevinzakka/form2fit/blob/HEAD/form2fit/code/ml/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4857c0ceac78f595","mcp_get_code":{"code_sha256":"4857c0ceac78f595"}},{"arxiv_id":"1910.13675","paper":"/paper/form2fit-learning-shape-priors-for","title":"Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly","date":"2019-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevinzakka/form2fit","path":"form2fit/code/ml/metrics.py","file_url":"https://github.com/kevinzakka/form2fit/blob/HEAD/form2fit/code/ml/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d65b505d16942e5e","mcp_get_code":{"code_sha256":"d65b505d16942e5e"}},{"arxiv_id":"1802.04742","paper":"/paper/quantifying-uncertainty-in-discrete","title":"Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning","date":"2018-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tjvandal/discrete-continuous-bdl","path":"tf_utils.py","file_url":"https://github.com/tjvandal/discrete-continuous-bdl/blob/HEAD/tf_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":"d7b358161d9d8226","mcp_get_code":{"code_sha256":"d7b358161d9d8226"}}]}