{"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/download-data","entry":"download_data","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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"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":"2209.04947","paper":"/paper/kernel-learning-for-explainable-climate","title":"Kernel Learning for Explainable Climate Science","date":"2022-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenzaxtazi/climate-kernel-learning","path":"utils/dataprep.py","file_url":"https://github.com/kenzaxtazi/climate-kernel-learning/blob/HEAD/utils/dataprep.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ef47618e97b5780","mcp_get_code":{"code_sha256":"5ef47618e97b5780"}},{"arxiv_id":"2006.02334","paper":"/paper/detectors-detecting-objects-with-recursive-1","title":"DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution","date":"2020-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/tf-models","path":"finegan/dataset.py","file_url":"https://github.com/Vishal-V/tf-models/blob/HEAD/finegan/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cabdcb9cf748ec42","mcp_get_code":{"code_sha256":"cabdcb9cf748ec42"}},{"arxiv_id":"1805.09821","paper":"/paper/a-corpus-for-multilingual-document","title":"A Corpus for Multilingual Document Classification in Eight Languages","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"n-waves/multifit","path":"prepare_xnli.py","file_url":"https://github.com/n-waves/multifit/blob/HEAD/prepare_xnli.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fea81e299034679b","mcp_get_code":{"code_sha256":"fea81e299034679b"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Soonhwan-Kwon/capsnet.mxnet","path":"capsulenet.py","file_url":"https://github.com/Soonhwan-Kwon/capsnet.mxnet/blob/HEAD/capsulenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"22def68ffad5b584","mcp_get_code":{"code_sha256":"22def68ffad5b584"}},{"arxiv_id":"1702.01983","paper":"/paper/face-aging-with-conditional-generative","title":"Face Aging With Conditional Generative Adversarial Networks","date":"2017-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/GSoC-TensorFlow-2019","path":"face_app/data_download.py","file_url":"https://github.com/Vishal-V/GSoC-TensorFlow-2019/blob/HEAD/face_app/data_download.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":"c8dea6dd84ed3e6b","mcp_get_code":{"code_sha256":"c8dea6dd84ed3e6b"}},{"arxiv_id":"1612.03242","paper":"/paper/stackgan-text-to-photo-realistic-image","title":"StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2016-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/GSoC","path":"face_app/data_download.py","file_url":"https://github.com/Vishal-V/GSoC/blob/HEAD/face_app/data_download.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":"c8dea6dd84ed3e6b","mcp_get_code":{"code_sha256":"c8dea6dd84ed3e6b"}},{"arxiv_id":"1612.03242","paper":"/paper/stackgan-text-to-photo-realistic-image","title":"StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2016-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/StackGAN","path":"data_download.py","file_url":"https://github.com/Vishal-V/StackGAN/blob/HEAD/data_download.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ff2c5c521817cf06","mcp_get_code":{"code_sha256":"ff2c5c521817cf06"}}]}