{"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/accuracy-2","entry":"Accuracy","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":7,"n_papers_ran":6,"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":8,"n_samples_ran":7,"n_samples_fingerprinted":3,"n_places":9,"n_places_pointer_only":5,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":3,"ran":2,"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":"2312.13032","paper":"/paper/nodemixup-tackling-under-reaching-for-graph","title":"NodeMixup: Tackling Under-Reaching for Graph Neural Networks","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weiganglu/nodemixup","path":"utils.py","file_url":"https://github.com/weiganglu/nodemixup/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01fe608f304f43c8","mcp_get_code":{"code_sha256":"01fe608f304f43c8"}},{"arxiv_id":"2310.05725","paper":"/paper/post-hoc-bias-scoring-is-optimal-for-fair","title":"Post-hoc Bias Scoring Is Optimal For Fair Classification","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenw20/biasscore","path":"postprocess_dp.py","file_url":"https://github.com/chenw20/biasscore/blob/HEAD/postprocess_dp.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36e09db3d9eee8a3","mcp_get_code":{"code_sha256":"36e09db3d9eee8a3"}},{"arxiv_id":"2208.10608","paper":"/paper/ribac-towards-robust-and-imperceptible","title":"RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN","date":"2022-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huyvnphan/ECCV2022-RIBAC","path":"modules/backdoor_module.py","file_url":"https://github.com/huyvnphan/ECCV2022-RIBAC/blob/HEAD/modules/backdoor_module.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7b2449cabc536ba","mcp_get_code":{"code_sha256":"d7b2449cabc536ba"}},{"arxiv_id":"2003.07311","paper":"/paper/cldice-a-topology-preserving-loss-function","title":"clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation","date":"2020-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nstucki/betti-matching","path":"evaluation.py","file_url":"https://github.com/nstucki/betti-matching/blob/HEAD/evaluation.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79bbfecce449d692","mcp_get_code":{"code_sha256":"79bbfecce449d692"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DevSinghSachan/multilingual_nmt","path":"models/transformer.py","file_url":"https://github.com/DevSinghSachan/multilingual_nmt/blob/HEAD/models/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a73559c6f2f6deb2","mcp_get_code":{"code_sha256":"a73559c6f2f6deb2"}},{"arxiv_id":"1507.05717","paper":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","date":"2015-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oyxhust/CNN-LSTM-CTC-text-recognition","path":"train_lstm.py","file_url":"https://github.com/oyxhust/CNN-LSTM-CTC-text-recognition/blob/HEAD/train_lstm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c1f5f90a5fe029c1","mcp_get_code":{"code_sha256":"c1f5f90a5fe029c1"}},{"arxiv_id":"1507.05717","paper":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","date":"2015-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oyxhust/CNN-LSTM-CTC-text-recognition","path":"train_bi_lstm.py","file_url":"https://github.com/oyxhust/CNN-LSTM-CTC-text-recognition/blob/HEAD/train_bi_lstm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"39b54be8c212cdc0","mcp_get_code":{"code_sha256":"39b54be8c212cdc0"}},{"arxiv_id":"1507.05717","paper":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","date":"2015-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oyxhust/CNN-LSTM-CTC-text-recognition","path":"train_crnn.py","file_url":"https://github.com/oyxhust/CNN-LSTM-CTC-text-recognition/blob/HEAD/train_crnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7688c4824425a1ea","mcp_get_code":{"code_sha256":"7688c4824425a1ea"}},{"arxiv_id":"aaai_29328","paper":null,"title":"arXiv:aaai_29328","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"WeigangLu/NodeMixup","path":"utils.py","file_url":"https://github.com/WeigangLu/NodeMixup/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01fe608f304f43c8","mcp_get_code":{"code_sha256":"01fe608f304f43c8"}}]}