{"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/shrink","entry":"shrink","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":2,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"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":"2305.18577","paper":"/paper/towards-constituting-mathematical-structures","title":"Towards Constituting Mathematical Structures for Learning to Optimize","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xhchrn/MS4L2O","path":"optimizers/ada_lista.py","file_url":"https://github.com/xhchrn/MS4L2O/blob/HEAD/optimizers/ada_lista.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70c15547a7bf67f1","mcp_get_code":{"code_sha256":"70c15547a7bf67f1"}},{"arxiv_id":"2110.15900","paper":"/paper/hyperparameter-tuning-is-all-you-need-for","title":"Hyperparameter Tuning is All You Need for LISTA","date":"2021-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/hyperlista","path":"models/ada_lista.py","file_url":"https://github.com/vita-group/hyperlista/blob/HEAD/models/ada_lista.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70c15547a7bf67f1","mcp_get_code":{"code_sha256":"70c15547a7bf67f1"}},{"arxiv_id":"2107.14398","paper":"/paper/on-the-interpretation-of-linear-riemannian","title":"On the interpretation of linear Riemannian tangent space model parameters in M/EEG","date":"2021-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rkobler/interpret_lin_rts_mdls","path":"library/spfiltering.py","file_url":"https://github.com/rkobler/interpret_lin_rts_mdls/blob/HEAD/library/spfiltering.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":"28cc7c824c8456e3","mcp_get_code":{"code_sha256":"28cc7c824c8456e3"}},{"arxiv_id":"1001.2363","paper":"/paper/stable-principal-component-pursuit","title":"Stable Principal Component Pursuit","date":null,"month_inferred_from_arxiv_id":"2010-01","title_source":"archive","repo":"dlegor/rad","path":"rad/_RobustDeepAutoencoder.py","file_url":"https://github.com/dlegor/rad/blob/HEAD/rad/_RobustDeepAutoencoder.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":"1ca924a0d2c71cea","mcp_get_code":{"code_sha256":"1ca924a0d2c71cea"}},{"arxiv_id":"0912.3599","paper":"/paper/robust-principal-component-analysis","title":"Robust Principal Component Analysis?","date":null,"month_inferred_from_arxiv_id":"2009-12","title_source":"archive","repo":"dfm/pcp","path":"pcp.py","file_url":"https://github.com/dfm/pcp/blob/HEAD/pcp.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f18a08dcc9f28f41","mcp_get_code":{"code_sha256":"f18a08dcc9f28f41"}}]}