{"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/get-subdict","entry":"get_subdict","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":0,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2403.06768","paper":"/paper/xb-maml-learning-expandable-basis-parameters","title":"XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"johnjaejunlee95/xb-maml","path":"model/resnet.py","file_url":"https://github.com/johnjaejunlee95/xb-maml/blob/HEAD/model/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ff7f408babc7b58","mcp_get_code":{"code_sha256":"4ff7f408babc7b58"}},{"arxiv_id":"2312.04028","paper":"/paper/imface-a-sophisticated-nonlinear-3d-morphable","title":"ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingwuzheng/imface","path":"model/modules.py","file_url":"https://github.com/mingwuzheng/imface/blob/HEAD/model/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba48e65ad184a4b7","mcp_get_code":{"code_sha256":"ba48e65ad184a4b7"}},{"arxiv_id":"2310.05174","paper":"/paper/gslb-the-graph-structure-learning-benchmark-1","title":"GSLB: The Graph Structure Learning Benchmark","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GSL-Benchmark/GSLB","path":"encoder/metamodule.py","file_url":"https://github.com/GSL-Benchmark/GSLB/blob/HEAD/encoder/metamodule.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cc9c74f5df741aef","mcp_get_code":{"code_sha256":"cc9c74f5df741aef"}},{"arxiv_id":"2303.14092","paper":"/paper/neuface-realistic-3d-neural-face-rendering","title":"NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aejion/neuface","path":"models/modules.py","file_url":"https://github.com/aejion/neuface/blob/HEAD/models/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"610a630c654d9d50","mcp_get_code":{"code_sha256":"610a630c654d9d50"}},{"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/maml.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/maml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"660433bc1546c183","mcp_get_code":{"code_sha256":"660433bc1546c183"}},{"arxiv_id":"2006.12504","paper":"/paper/the-gce-in-a-new-light-disentangling-the-g","title":"The GCE in a New Light: Disentangling the $γ$-ray Sky with Bayesian Graph Convolutional Neural Networks","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FloList/GCE_NN","path":"GCE/parameter_utils.py","file_url":"https://github.com/FloList/GCE_NN/blob/HEAD/GCE/parameter_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c79e4abf4595b79f","mcp_get_code":{"code_sha256":"c79e4abf4595b79f"}},{"arxiv_id":"2006.09661","paper":"/paper/implicit-neural-representations-with-periodic","title":"Implicit Neural Representations with Periodic Activation Functions","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TalFurman/Implict_neural_representation_of_images","path":"modules.py","file_url":"https://github.com/TalFurman/Implict_neural_representation_of_images/blob/HEAD/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc9c74f5df741aef","mcp_get_code":{"code_sha256":"cc9c74f5df741aef"}}]}