{"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/find-first-pos","entry":"find_first_pos","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":5,"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":1,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":0},"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":"2106.04217","paper":"/paper/dynamic-sparse-training-for-deep","title":"Dynamic Sparse Training for Deep Reinforcement Learning","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GhadaSokar/Dynamic-Sparse-Training-for-Deep-Reinforcement-Learning","path":"sparse_utils.py","file_url":"https://github.com/GhadaSokar/Dynamic-Sparse-Training-for-Deep-Reinforcement-Learning/blob/HEAD/sparse_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09540a28779b66ce","mcp_get_code":{"code_sha256":"09540a28779b66ce"}},{"arxiv_id":"2006.14085","paper":"/paper/topological-insights-in-sparse-neural","title":"Topological Insights into Sparse Neural Networks","date":"2020-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"09540a28779b66ce","mcp_get_code":{"code_sha256":"09540a28779b66ce"}},{"arxiv_id":"1903.07138","paper":"/paper/evolving-and-understanding-sparse-deep-neural","title":"A Brain-inspired Algorithm for Training Highly Sparse Neural Networks","date":"2019-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joostPieterse/CosineSET","path":"cosine_set_fixprob_dense_mlp.py","file_url":"https://github.com/joostPieterse/CosineSET/blob/HEAD/cosine_set_fixprob_dense_mlp.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"09540a28779b66ce","mcp_get_code":{"code_sha256":"09540a28779b66ce"}},{"arxiv_id":"1901.09181","paper":"/paper/sparse-evolutionary-deep-learning-with-over","title":"Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware","date":"2019-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"09540a28779b66ce","mcp_get_code":{"code_sha256":"09540a28779b66ce"}},{"arxiv_id":"1707.04780","paper":"/paper/scalable-training-of-artificial-neural","title":"Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science","date":"2017-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"09540a28779b66ce","mcp_get_code":{"code_sha256":"09540a28779b66ce"}}]}