{"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/create-data","entry":"create_data","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":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":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"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":"2411.06311","paper":"/paper/when-are-dynamical-systems-learned-from-time","title":"When are dynamical systems learned from time series data statistically accurate?","date":"2024-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ni-sha-c/stacNODE","path":"src/util.py","file_url":"https://github.com/ni-sha-c/stacNODE/blob/HEAD/src/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8153624831bf335c","mcp_get_code":{"code_sha256":"8153624831bf335c"}},{"arxiv_id":"2308.02753","paper":"/paper/damstf-domain-adversarial-learning-enhanced","title":"DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation","date":"2023-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruidan/DAS","path":"code/read_amazon.py","file_url":"https://github.com/ruidan/DAS/blob/HEAD/code/read_amazon.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3bcb8caa0317b7c2","mcp_get_code":{"code_sha256":"3bcb8caa0317b7c2"}},{"arxiv_id":"2203.01629","paper":"/paper/continuous-relaxation-for-the-multivariate","title":"Learning Group Importance using the Differentiable Hypergeometric Distribution","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thomassutter/mvhg","path":"main_minimal_app.py","file_url":"https://github.com/thomassutter/mvhg/blob/HEAD/main_minimal_app.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9004c814450d0d7","mcp_get_code":{"code_sha256":"e9004c814450d0d7"}},{"arxiv_id":"1811.08162","paper":"/paper/deepzip-lossless-data-compression-using","title":"DeepZip: Lossless Data Compression using Recurrent Neural Networks","date":"2018-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mohit1997/DeepZip","path":"src/decompressor.py","file_url":"https://github.com/mohit1997/DeepZip/blob/HEAD/src/decompressor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8157c6c0bc45b3e3","mcp_get_code":{"code_sha256":"8157c6c0bc45b3e3"}},{"arxiv_id":"1805.08402","paper":"/paper/adapted-deep-embeddings-a-synthesis-of-1","title":"Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning","date":"2018-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garyCC227/thesis","path":"model_train.py","file_url":"https://github.com/garyCC227/thesis/blob/HEAD/model_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"398f6908e33d3b75","mcp_get_code":{"code_sha256":"398f6908e33d3b75"}}]}