{"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-net","entry":"get_net","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":3,"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":7,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"unverified":4},"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":"2605.11884","paper":"/paper/arxiv-2605-11884","title":"Sobolev Regularized MMD Gradient Flow","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"FerdTian/SrMMD","path":"src/generative/student_teacher/trainer.py","file_url":"https://github.com/FerdTian/SrMMD/blob/HEAD/src/generative/student_teacher/trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f250cec6f2ad6f3","mcp_get_code":{"code_sha256":"5f250cec6f2ad6f3"}},{"arxiv_id":"2409.14980","paper":"/paper/de-regularized-maximum-mean-discrepancy","title":"(De)-regularized Maximum Mean Discrepancy Gradient Flow","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hudsonchen/drmmd","path":"student_teacher/trainer.py","file_url":"https://github.com/hudsonchen/drmmd/blob/HEAD/student_teacher/trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f250cec6f2ad6f3","mcp_get_code":{"code_sha256":"5f250cec6f2ad6f3"}},{"arxiv_id":"2402.02316","paper":"/paper/your-diffusion-model-is-secretly-a","title":"Diffusion Models are Certifiably Robust Classifiers","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanranchen/NoisedDiffusionClassifiers","path":"defenses/HighLevelGuidedDenoiser/inceptionresnet.py","file_url":"https://github.com/huanranchen/NoisedDiffusionClassifiers/blob/HEAD/defenses/HighLevelGuidedDenoiser/inceptionresnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f995d502b1dc9eac","mcp_get_code":{"code_sha256":"f995d502b1dc9eac"}},{"arxiv_id":"2402.02316","paper":"/paper/your-diffusion-model-is-secretly-a","title":"Diffusion Models are Certifiably Robust Classifiers","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanranchen/NoisedDiffusionClassifiers","path":"defenses/HighLevelGuidedDenoiser/resnet.py","file_url":"https://github.com/huanranchen/NoisedDiffusionClassifiers/blob/HEAD/defenses/HighLevelGuidedDenoiser/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d84e622ec1d0d9f7","mcp_get_code":{"code_sha256":"d84e622ec1d0d9f7"}},{"arxiv_id":"2310.07152","paper":"/paper/no-privacy-left-outside-on-the-in-security-of","title":"No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ziqi-zhang/teeslice-artifact","path":"membership-inference/knockoff_train.py","file_url":"https://github.com/ziqi-zhang/teeslice-artifact/blob/HEAD/membership-inference/knockoff_train.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"64e9bca534ec9a24","mcp_get_code":{"code_sha256":"64e9bca534ec9a24"}},{"arxiv_id":"2202.01402","paper":"/paper/galaxy-graph-based-active-learning-at-the","title":"GALAXY: Graph-based Active Learning at the Extreme","date":"2022-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jifanz/GALAXY","path":"src/bait/model.py","file_url":"https://github.com/jifanz/GALAXY/blob/HEAD/src/bait/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"98b6004d6b335def","mcp_get_code":{"code_sha256":"98b6004d6b335def"}},{"arxiv_id":"2110.04545","paper":"/paper/towards-data-free-domain-generalization","title":"Towards Data-Free Domain Generalization","date":"2021-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaokunChen245/DFDG","path":"dfdg/training/train_student_syn_img.py","file_url":"https://github.com/HaokunChen245/DFDG/blob/HEAD/dfdg/training/train_student_syn_img.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":"9966b8ec681f4404","mcp_get_code":{"code_sha256":"9966b8ec681f4404"}},{"arxiv_id":"1906.04370","paper":"/paper/maximum-mean-discrepancy-gradient-flow","title":"Maximum Mean Discrepancy Gradient Flow","date":"2019-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MichaelArbel/MMD-gradient-flow","path":"trainer.py","file_url":"https://github.com/MichaelArbel/MMD-gradient-flow/blob/HEAD/trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5f250cec6f2ad6f3","mcp_get_code":{"code_sha256":"5f250cec6f2ad6f3"}},{"arxiv_id":"1803.07031","paper":"/paper/factorised-spatial-representation-learning","title":"Factorised spatial representation learning: application in semi-supervised myocardial segmentation","date":"2018-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agis85/spatial_factorisation","path":"sdnet.py","file_url":"https://github.com/agis85/spatial_factorisation/blob/HEAD/sdnet.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":"a9aaff12acd6c28d","mcp_get_code":{"code_sha256":"a9aaff12acd6c28d"}}]}