{"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/sqrt","entry":"sqrt","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":8,"n_papers_ran":1,"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":1,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":7},"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":"2304.04555","paper":"/paper/neural-diffeomorphic-non-uniform-b-spline","title":"Neural Diffeomorphic Non-uniform B-spline Flows","date":"2023-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smhongok/Non-uniform-B-spline-Flow","path":"bgflow/bgflow/nn/flow/transformer/bspline.py","file_url":"https://github.com/smhongok/Non-uniform-B-spline-Flow/blob/HEAD/bgflow/bgflow/nn/flow/transformer/bspline.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b58d01a29ee581a","mcp_get_code":{"code_sha256":"0b58d01a29ee581a"}},{"arxiv_id":"2207.09453","paper":"/paper/e3nn-euclidean-neural-networks","title":"e3nn: Euclidean Neural Networks","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atomicarchitects/phonax","path":"phonax/gradio.py","file_url":"https://github.com/atomicarchitects/phonax/blob/HEAD/phonax/gradio.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1c715fe36ed81d4d","mcp_get_code":{"code_sha256":"1c715fe36ed81d4d"}},{"arxiv_id":"2203.08958","paper":"/paper/on-the-usefulness-of-the-fit-on-the-test-view","title":"On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"markus93/fit-on-the-test","path":"Experiments_Pseudo/calibration_functions.py","file_url":"https://github.com/markus93/fit-on-the-test/blob/HEAD/Experiments_Pseudo/calibration_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"645602835255b5f9","mcp_get_code":{"code_sha256":"645602835255b5f9"}},{"arxiv_id":"2107.14351","paper":"/paper/contemporary-symbolic-regression-methods-and","title":"Contemporary Symbolic Regression Methods and their Relative Performance","date":"2021-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"philipp238/parfam","path":"src/parfam/utils.py","file_url":"https://github.com/philipp238/parfam/blob/HEAD/src/parfam/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6623611f6314bd65","mcp_get_code":{"code_sha256":"6623611f6314bd65"}},{"arxiv_id":"2107.12436","paper":"/paper/feature-synergy-redundancy-and-independence","title":"Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition","date":"2021-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BCG-Gamma/facet","path":"src/facet/inspection/_shap_context.py","file_url":"https://github.com/BCG-Gamma/facet/blob/HEAD/src/facet/inspection/_shap_context.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":"1a8b5ab61024ac45","mcp_get_code":{"code_sha256":"1a8b5ab61024ac45"}},{"arxiv_id":"1912.00965","paper":"/paper/ap-perf-incorporating-generic-performance","title":"AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning","date":"2019-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rizalzaf/ap_perf","path":"ap_perf/expression.py","file_url":"https://github.com/rizalzaf/ap_perf/blob/HEAD/ap_perf/expression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bee26ca4d596099b","mcp_get_code":{"code_sha256":"bee26ca4d596099b"}},{"arxiv_id":"1906.01549","paper":"/paper/streaming-variational-monte-carlo","title":"Streaming Variational Monte Carlo","date":"2019-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"catniplab/svmc","path":"svmc/operation.py","file_url":"https://github.com/catniplab/svmc/blob/HEAD/svmc/operation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c44d1481a7bc874c","mcp_get_code":{"code_sha256":"c44d1481a7bc874c"}},{"arxiv_id":"Ma_ReMP-AD_Retrieval-enhanced_Multi-modal_Prompt_Fusion_for_Few-Shot_Industrial_Visual_Anomaly_ICCV_2025_paper","paper":null,"title":"arXiv:Ma_ReMP-AD_Retrieval-enhanced_Multi-modal_Prompt_Fusion_for_Few-Shot_Industrial_Visual_Anomaly_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cshcma/ReMP-AD","path":"modules/scoring.py","file_url":"https://github.com/cshcma/ReMP-AD/blob/HEAD/modules/scoring.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f65912ae9a8e79f0","mcp_get_code":{"code_sha256":"f65912ae9a8e79f0"}}]}