{"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/ensemble-2","entry":"Ensemble","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":5,"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":5,"n_samples_fingerprinted":2,"n_places":6,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"unverified":1},"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":"2507.23771","paper":null,"title":"arXiv:2507.23771","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"justinkay/coda","path":"coda/coda.py","file_url":"https://github.com/justinkay/coda/blob/HEAD/coda/coda.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"072f6cd34ba1890f","mcp_get_code":{"code_sha256":"072f6cd34ba1890f"}},{"arxiv_id":"2502.02538","paper":"/paper/flow-q-learning","title":"Flow Q-Learning","date":"2025-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MohammadrezaNakhaei/FQL","path":"fql.py","file_url":"https://github.com/MohammadrezaNakhaei/FQL/blob/HEAD/fql.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b3e1ba477f66ccd1","mcp_get_code":{"code_sha256":"b3e1ba477f66ccd1"}},{"arxiv_id":"2104.00671","paper":"/paper/trs-transferability-reduced-ensemble-via","title":"TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI-secure/Transferability-Reduced-Smooth-Ensemble","path":"train/Empirical/trainer.py","file_url":"https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble/blob/HEAD/train/Empirical/trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"26fd974a366fee7c","mcp_get_code":{"code_sha256":"26fd974a366fee7c"}},{"arxiv_id":"2010.08830","paper":"/paper/mesa-boost-ensemble-imbalanced-learning-with","title":"MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler","date":"2020-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NeurIPS2020AnonymousSubmission/mesa","path":"mesa.py","file_url":"https://github.com/NeurIPS2020AnonymousSubmission/mesa/blob/HEAD/mesa.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1ebf5a998d2091f","mcp_get_code":{"code_sha256":"c1ebf5a998d2091f"}},{"arxiv_id":"1805.12114","paper":"/paper/deep-reinforcement-learning-in-a-handful-of","title":"Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models","date":"2018-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ByMic/PETS","path":"utils/ensemble.py","file_url":"https://github.com/ByMic/PETS/blob/HEAD/utils/ensemble.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c0e747272c22445","mcp_get_code":{"code_sha256":"1c0e747272c22445"}},{"arxiv_id":"1612.01474","paper":"/paper/simple-and-scalable-predictive-uncertainty","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","date":"2016-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"StanfordASL/SCOD","path":"nn_ood/posteriors/ensemble.py","file_url":"https://github.com/StanfordASL/SCOD/blob/HEAD/nn_ood/posteriors/ensemble.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfe90024d387377b","mcp_get_code":{"code_sha256":"cfe90024d387377b"}}]}