{"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/computegrammatrix","entry":"computeGramMatrix","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":8,"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":2,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":0},"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":"2602.17947","paper":"/paper/arxiv-2602-17947","title":"Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"kjunelee/MetaOptNet","path":"models/classification_heads.py","file_url":"https://github.com/kjunelee/MetaOptNet/blob/HEAD/models/classification_heads.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6134727f9d286287","mcp_get_code":{"code_sha256":"6134727f9d286287"}},{"arxiv_id":"2310.10207","paper":"/paper/bongard-openworld-few-shot-reasoning-for-free","title":"Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joyjayng/Bongard-OpenWorld","path":"models/head/metaoptnet.py","file_url":"https://github.com/joyjayng/Bongard-OpenWorld/blob/HEAD/models/head/metaoptnet.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":"691b2fde39c5dae5","mcp_get_code":{"code_sha256":"691b2fde39c5dae5"}},{"arxiv_id":"2211.14666","paper":"/paper/synergies-between-disentanglement-and","title":"Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tristandeleu/synergies-disentanglement-sparsity","path":"metaoptnet/models/classification_heads.py","file_url":"https://github.com/tristandeleu/synergies-disentanglement-sparsity/blob/HEAD/metaoptnet/models/classification_heads.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"691b2fde39c5dae5","mcp_get_code":{"code_sha256":"691b2fde39c5dae5"}},{"arxiv_id":"2111.04316","paper":"/paper/sega-semantic-guided-attention-on-visual","title":"SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning","date":"2021-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martayang/sega","path":"models/classification_heads.py","file_url":"https://github.com/martayang/sega/blob/HEAD/models/classification_heads.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6134727f9d286287","mcp_get_code":{"code_sha256":"6134727f9d286287"}},{"arxiv_id":"2010.07092","paper":"/paper/data-augmentation-for-meta-learning-1","title":"Data Augmentation for Meta-Learning","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RenkunNi/MetaAug","path":"models/classification_heads.py","file_url":"https://github.com/RenkunNi/MetaAug/blob/HEAD/models/classification_heads.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6134727f9d286287","mcp_get_code":{"code_sha256":"6134727f9d286287"}},{"arxiv_id":"2010.00763","paper":"/paper/bongard-logo-a-new-benchmark-for-human-level","title":"Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/Bongard-LOGO","path":"Bongard-LOGO_Baselines/models/metaOptNet.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/metaOptNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"691b2fde39c5dae5","mcp_get_code":{"code_sha256":"691b2fde39c5dae5"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andrewbo29/mtm-meta-learning-sa","path":"protonet/models/classification_heads.py","file_url":"https://github.com/andrewbo29/mtm-meta-learning-sa/blob/HEAD/protonet/models/classification_heads.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6134727f9d286287","mcp_get_code":{"code_sha256":"6134727f9d286287"}},{"arxiv_id":"ijcai2020_0377","paper":null,"title":"arXiv:ijcai2020_0377","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"P1nzhuo/Consistent-MetaReg","path":"models/bilevel_head.py","file_url":"https://github.com/P1nzhuo/Consistent-MetaReg/blob/HEAD/models/bilevel_head.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"691b2fde39c5dae5","mcp_get_code":{"code_sha256":"691b2fde39c5dae5"}}]}