{"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/sinkhorndistance","entry":"SinkhornDistance","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":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":5,"n_samples_ran":4,"n_samples_fingerprinted":3,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"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":"2504.16275","paper":"/paper/quantum-doubly-stochastic-transformers","title":"Quantum Doubly Stochastic Transformers","date":"2025-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michaelsdr/sinkformers","path":"nlp-tutorial/text-classification-transformer/model_sym.py","file_url":"https://github.com/michaelsdr/sinkformers/blob/HEAD/nlp-tutorial/text-classification-transformer/model_sym.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0541009c68476312","mcp_get_code":{"code_sha256":"0541009c68476312"}},{"arxiv_id":"2408.01946","paper":"/paper/2408-01946","title":"Masked Angle-Aware Autoencoder for Remote Sensing Images","date":"2024-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"benesakitam/MA3E","path":"models_ma3e.py","file_url":"https://github.com/benesakitam/MA3E/blob/HEAD/models_ma3e.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"be5a3cfe3e1482ba","mcp_get_code":{"code_sha256":"be5a3cfe3e1482ba"}},{"arxiv_id":"2407.05364","paper":"/paper/ptarl-prototype-based-tabular-representation-1","title":"PTaRL: Prototype-based Tabular Representation Learning via Space Calibration","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alcoholrithm/PTaRL","path":"ptarl_lightning.py","file_url":"https://github.com/Alcoholrithm/PTaRL/blob/HEAD/ptarl_lightning.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76418a9a803865bb","mcp_get_code":{"code_sha256":"76418a9a803865bb"}},{"arxiv_id":"2305.13803","paper":"/paper/norm-knowledge-distillation-via-n-to-one","title":"NORM: Knowledge Distillation via N-to-One Representation Matching","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"osvai/norm","path":"distiller_zoo/FitNet.py","file_url":"https://github.com/osvai/norm/blob/HEAD/distiller_zoo/FitNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fcabf3b0cfff6f97","mcp_get_code":{"code_sha256":"fcabf3b0cfff6f97"}},{"arxiv_id":"2103.14259","paper":"/paper/ota-optimal-transport-assignment-for-object","title":"OTA: Optimal Transport Assignment for Object Detection","date":"2021-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Megvii-BaseDetection/OTA","path":"playground/detection/coco/ota.res50.fpn.coco.800size.1x/fcos.py","file_url":"https://github.com/Megvii-BaseDetection/OTA/blob/HEAD/playground/detection/coco/ota.res50.fpn.coco.800size.1x/fcos.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7b7656a308d5ef77","mcp_get_code":{"code_sha256":"7b7656a308d5ef77"}}]}