{"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/fft2","entry":"fft2","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":5,"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":4,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":2},"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":"2403.17042","paper":"/paper/provably-robust-score-based-diffusion","title":"Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction","date":"2024-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"x1xu/diffusion-plug-and-play","path":"util/img_utils.py","file_url":"https://github.com/x1xu/diffusion-plug-and-play/blob/HEAD/util/img_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"19cc8241bdc7b50a","mcp_get_code":{"code_sha256":"19cc8241bdc7b50a"}},{"arxiv_id":"2402.16907","paper":"/paper/diffusion-posterior-proximal-sampling-for","title":"Diffusion Posterior Proximal Sampling for Image Restoration","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"74587887/dpps_code","path":"util/img_utils.py","file_url":"https://github.com/74587887/dpps_code/blob/HEAD/util/img_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"19cc8241bdc7b50a","mcp_get_code":{"code_sha256":"19cc8241bdc7b50a"}},{"arxiv_id":"2312.10271","paper":"/paper/robustness-of-deep-learning-for-accelerated","title":"Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data","date":"2023-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mli-lab/mri_data_diversity","path":"convert_datasets/cc-359/convert.py","file_url":"https://github.com/mli-lab/mri_data_diversity/blob/HEAD/convert_datasets/cc-359/convert.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"49fb101a835c692c","mcp_get_code":{"code_sha256":"49fb101a835c692c"}},{"arxiv_id":"2307.14362","paper":"/paper/learnable-wavelet-neural-networks-for","title":"Learnable wavelet neural networks for cosmological inference","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Chris-Pedersen/LearnableWavelets","path":"learnable_wavelets/scattering/torch_backend.py","file_url":"https://github.com/Chris-Pedersen/LearnableWavelets/blob/HEAD/learnable_wavelets/scattering/torch_backend.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fcf37707f0675e0a","mcp_get_code":{"code_sha256":"fcf37707f0675e0a"}},{"arxiv_id":"2010.16262","paper":"/paper/experimental-design-for-mri-by-greedy-policy","title":"Experimental design for MRI by greedy policy search","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Timsey/pg_mri","path":"src/helpers/transforms.py","file_url":"https://github.com/Timsey/pg_mri/blob/HEAD/src/helpers/transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a768de7e61bd97d","mcp_get_code":{"code_sha256":"9a768de7e61bd97d"}}]}