{"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/norm2","entry":"norm2","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":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":6,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"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":"2405.18877","paper":"/paper/continuous-product-graph-neural-networks","title":"Continuous Product Graph Neural Networks","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arefeinizade2/citrus","path":"MetrLA_PemsBay/diffusion_net/geometry.py","file_url":"https://github.com/arefeinizade2/citrus/blob/HEAD/MetrLA_PemsBay/diffusion_net/geometry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"169c2afe3d0207b9","mcp_get_code":{"code_sha256":"169c2afe3d0207b9"}},{"arxiv_id":"2212.06079","paper":"/paper/robust-perception-through-equivariance","title":"Robust Perception through Equivariance","date":"2022-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-columbia/equi4rob","path":"learning/sentive_loss.py","file_url":"https://github.com/cvlab-columbia/equi4rob/blob/HEAD/learning/sentive_loss.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e0900a2734c87d4","mcp_get_code":{"code_sha256":"0e0900a2734c87d4"}},{"arxiv_id":"2212.05023","paper":"/paper/mesh-neural-networks-for-se-3-equivariant","title":"Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall","date":"2022-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sukjulian/coronary-mesh-convolution","path":"diffusion_net/geometry.py","file_url":"https://github.com/sukjulian/coronary-mesh-convolution/blob/HEAD/diffusion_net/geometry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"169c2afe3d0207b9","mcp_get_code":{"code_sha256":"169c2afe3d0207b9"}},{"arxiv_id":"2202.12295","paper":"/paper/factorizer-a-scalable-interpretable-approach","title":"Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation","date":"2022-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pashtari/factorizer","path":"factorizer/factorization/operations.py","file_url":"https://github.com/pashtari/factorizer/blob/HEAD/factorizer/factorization/operations.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":"76ce63dfb0219599","mcp_get_code":{"code_sha256":"76ce63dfb0219599"}},{"arxiv_id":"2110.01823","paper":"/paper/adversarial-attacks-on-black-box-video","title":"Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sli057/Geo-TRAP","path":"query_attack/query_frame_util.py","file_url":"https://github.com/sli057/Geo-TRAP/blob/HEAD/query_attack/query_frame_util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"49c4e33835060f89","mcp_get_code":{"code_sha256":"49c4e33835060f89"}},{"arxiv_id":"2003.07637","paper":"/paper/motion-excited-sampler-video-adversarial","title":"Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior","date":"2020-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaofanustc/ME-Sampler","path":"blackbox_attack.py","file_url":"https://github.com/xiaofanustc/ME-Sampler/blob/HEAD/blackbox_attack.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":"febe202057d1d53f","mcp_get_code":{"code_sha256":"febe202057d1d53f"}},{"arxiv_id":"2001.00281","paper":"/paper/zeroq-a-novel-zero-shot-quantization","title":"ZeroQ: A Novel Zero Shot Quantization Framework","date":"2020-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jakc4103/DFQ","path":"improve_dfq.py","file_url":"https://github.com/jakc4103/DFQ/blob/HEAD/improve_dfq.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1392852bee3f1e32","mcp_get_code":{"code_sha256":"1392852bee3f1e32"}}]}