{"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/ulbcorner-rot","entry":"ULBcorner_rot","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":3,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":3},"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":"2311.07143","paper":"/paper/learning-symmetrization-for-equivariance-with","title":"Learning Symmetrization for Equivariance with Orbit Distance Minimization","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tiendatnguyen-vision/orbit-symmetrize","path":"RotatedMNIST/LPS_orbit/emlp-pytorch/emlp_pytorch/datasets.py","file_url":"https://github.com/tiendatnguyen-vision/orbit-symmetrize/blob/HEAD/RotatedMNIST/LPS_orbit/emlp-pytorch/emlp_pytorch/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b86e1ff7ae6d8db","mcp_get_code":{"code_sha256":"3b86e1ff7ae6d8db"}},{"arxiv_id":"2306.06327","paper":"/paper/any-dimensional-equivariant-neural-networks","title":"Any-dimensional equivariant neural networks","date":"2023-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mateodd25/free-nets","path":"emlp/datasets.py","file_url":"https://github.com/mateodd25/free-nets/blob/HEAD/emlp/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"afeb3056cc5cad08","mcp_get_code":{"code_sha256":"afeb3056cc5cad08"}},{"arxiv_id":"2204.00887","paper":"/paper/dimensionless-machine-learning-imposing-exact","title":"Dimensionless machine learning: Imposing exact units equivariance","date":"2022-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weichiyao/scalaremlp","path":"scalaremlp/datasets.py","file_url":"https://github.com/weichiyao/scalaremlp/blob/HEAD/scalaremlp/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"791a506bebdb19cf","mcp_get_code":{"code_sha256":"791a506bebdb19cf"}},{"arxiv_id":"2106.06610","paper":"/paper/scalars-are-universal-gauge-equivariant","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchworking/scalaremlp","path":"scalaremlp/datasets.py","file_url":"https://github.com/researchworking/scalaremlp/blob/HEAD/scalaremlp/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"791a506bebdb19cf","mcp_get_code":{"code_sha256":"791a506bebdb19cf"}},{"arxiv_id":"2104.09459","paper":"/paper/a-practical-method-for-constructing","title":"A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mfinzi/equivariant-MLP","path":"emlp/datasets.py","file_url":"https://github.com/mfinzi/equivariant-MLP/blob/HEAD/emlp/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"791a506bebdb19cf","mcp_get_code":{"code_sha256":"791a506bebdb19cf"}}]}