{"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/plot-conj","entry":"plot_conj","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":0,"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":1,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"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":"2210.12153","paper":"/paper/on-amortizing-convex-conjugates-for-optimal","title":"On amortizing convex conjugates for optimal transport","date":"2022-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/w2ot","path":"scripts/vis-2d-transport.py","file_url":"https://github.com/facebookresearch/w2ot/blob/HEAD/scripts/vis-2d-transport.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":"7fb9bfa9b31ec695","mcp_get_code":{"code_sha256":"7fb9bfa9b31ec695"}},{"arxiv_id":"2110.02999","paper":"/paper/generative-modeling-with-optimal-transport","title":"Generative Modeling with Optimal Transport Maps","date":"2021-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7fb9bfa9b31ec695","mcp_get_code":{"code_sha256":"7fb9bfa9b31ec695"}},{"arxiv_id":"2106.01954","paper":"/paper/do-neural-optimal-transport-solvers-work-a","title":"Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark","date":"2021-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7fb9bfa9b31ec695","mcp_get_code":{"code_sha256":"7fb9bfa9b31ec695"}},{"arxiv_id":"1909.13082","paper":"/paper/wasserstein-2-generative-networks","title":"Wasserstein-2 Generative Networks","date":"2019-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7fb9bfa9b31ec695","mcp_get_code":{"code_sha256":"7fb9bfa9b31ec695"}},{"arxiv_id":"1902.07197","paper":"/paper/2-wasserstein-approximation-via-restricted","title":"2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs","date":"2019-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7fb9bfa9b31ec695","mcp_get_code":{"code_sha256":"7fb9bfa9b31ec695"}}]}