{"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/getdata","entry":"getData","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":0,"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":9,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":9},"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":"2406.00418","paper":"/paper/gate-how-to-keep-out-intrusive-neighbors","title":"GATE: How to Keep Out Intrusive Neighbors","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RelationalML/GATE","path":"runExp_GAT(E).py","file_url":"https://github.com/RelationalML/GATE/blob/HEAD/runExp_GAT%28E%29.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"75754b0795a7682e","mcp_get_code":{"code_sha256":"75754b0795a7682e"}},{"arxiv_id":"2406.00418","paper":"/paper/gate-how-to-keep-out-intrusive-neighbors","title":"GATE: How to Keep Out Intrusive Neighbors","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"relationalml/gate","path":"runExp_RigidArch.py","file_url":"https://github.com/relationalml/gate/blob/HEAD/runExp_RigidArch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"17bf82238d48040f","mcp_get_code":{"code_sha256":"17bf82238d48040f"}},{"arxiv_id":"2103.16457","paper":"/paper/dream-a-fluid-kinetic-framework-for-tokamak","title":"DREAM: a fluid-kinetic framework for tokamak disruption runaway electron simulations","date":null,"month_inferred_from_arxiv_id":"2021-03","title_source":"archive","repo":"chalmersplasmatheory/DREAM","path":"py/DREAM/DREAMIO.py","file_url":"https://github.com/chalmersplasmatheory/DREAM/blob/HEAD/py/DREAM/DREAMIO.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24f3cb5f5d39a48a","mcp_get_code":{"code_sha256":"24f3cb5f5d39a48a"}},{"arxiv_id":"2006.00719","paper":"/paper/adahessian-an-adaptive-second-order-optimizer","title":"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning","date":"2020-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirgholami/adahessian","path":"image_classification/utils.py","file_url":"https://github.com/amirgholami/adahessian/blob/HEAD/image_classification/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52e6b930397c2338","mcp_get_code":{"code_sha256":"52e6b930397c2338"}},{"arxiv_id":"2005.09007","paper":"/paper/u-2-net-going-deeper-with-nested-u-structure","title":"U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Akhilesh64/Image-Segmentation-U-2-Net","path":"dataLoader.py","file_url":"https://github.com/Akhilesh64/Image-Segmentation-U-2-Net/blob/HEAD/dataLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21c24045e17c6985","mcp_get_code":{"code_sha256":"21c24045e17c6985"}},{"arxiv_id":"2005.09007","paper":"/paper/u-2-net-going-deeper-with-nested-u-structure","title":"U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Akhilesh64/Image-Segmentation-U-2-Net","path":"Scripts_DUTS/dataLoader.py","file_url":"https://github.com/Akhilesh64/Image-Segmentation-U-2-Net/blob/HEAD/Scripts_DUTS/dataLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8144398e708b679f","mcp_get_code":{"code_sha256":"8144398e708b679f"}},{"arxiv_id":"1912.07145","paper":"/paper/pyhessian-neural-networks-through-the-lens-of","title":"PyHessian: Neural Networks Through the Lens of the Hessian","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirgholami/pyhessian","path":"utils.py","file_url":"https://github.com/amirgholami/pyhessian/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f5d5616297ed7a9","mcp_get_code":{"code_sha256":"1f5d5616297ed7a9"}},{"arxiv_id":"1901.09997","paper":"/paper/quasi-newton-methods-for-deep-learning-forget","title":"Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample","date":"2019-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OptMLGroup/SQN","path":"data_generation.py","file_url":"https://github.com/OptMLGroup/SQN/blob/HEAD/data_generation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f5cb0801555fb37","mcp_get_code":{"code_sha256":"7f5cb0801555fb37"}},{"arxiv_id":"1606.00915","paper":"/paper/deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aldo-aguilar/nu-style","path":"CycleGan/src/dataset.py","file_url":"https://github.com/aldo-aguilar/nu-style/blob/HEAD/CycleGan/src/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1127bbe82e43e0e6","mcp_get_code":{"code_sha256":"1127bbe82e43e0e6"}}]}