{"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-trainer","entry":"get_trainer","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":3,"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":3,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2310.17914","paper":"/paper/3d-aware-visual-question-answering-about-1","title":"3D-Aware Visual Question Answering about Parts, Poses and Occlusions","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingruiwang/3d-aware-vqa","path":"PO3D-VQA/attr_net/trainer.py","file_url":"https://github.com/xingruiwang/3d-aware-vqa/blob/HEAD/PO3D-VQA/attr_net/trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"712891b9264da608","mcp_get_code":{"code_sha256":"712891b9264da608"}},{"arxiv_id":"2202.00155","paper":"/paper/fortuitous-forgetting-in-connectionist-1","title":"Fortuitous Forgetting in Connectionist Networks","date":"2022-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlml/fortuitous_forgetting","path":"llf_ke/train_KE_cls.py","file_url":"https://github.com/hlml/fortuitous_forgetting/blob/HEAD/llf_ke/train_KE_cls.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13e0b35909c0dfbc","mcp_get_code":{"code_sha256":"13e0b35909c0dfbc"}},{"arxiv_id":"2106.08882","paper":"/paper/robust-training-in-high-dimensions-via-block","title":"Robust Training in High Dimensions via Block Coordinate Geometric Median Descent","date":"2021-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anishacharya/Optimization-Mavericks","path":"optimization_driver.py","file_url":"https://github.com/anishacharya/Optimization-Mavericks/blob/HEAD/optimization_driver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e1a394c4f364f7f","mcp_get_code":{"code_sha256":"7e1a394c4f364f7f"}},{"arxiv_id":"2103.05152","paper":"/paper/knowledge-evolution-in-neural-networks","title":"Knowledge Evolution in Neural Networks","date":"2021-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ahmdtaha/knowledge_evolution","path":"KE_model.py","file_url":"https://github.com/ahmdtaha/knowledge_evolution/blob/HEAD/KE_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2e692a0113e3d709","mcp_get_code":{"code_sha256":"2e692a0113e3d709"}},{"arxiv_id":"2003.13045","paper":"/paper/learning-by-analogy-reliable-supervision-from","title":"Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation","date":"2020-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lliuz/ARFlow","path":"trainer/get_trainer.py","file_url":"https://github.com/lliuz/ARFlow/blob/HEAD/trainer/get_trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c99e97f4d4cb84e","mcp_get_code":{"code_sha256":"5c99e97f4d4cb84e"}}]}