{"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/rop","entry":"Rop","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":1,"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":3,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":1,"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":"2410.22113","paper":"/paper/where-do-large-learning-rates-lead-us","title":"Where Do Large Learning Rates Lead Us?","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isadrtdinov/understanding-large-lrs","path":"convolutional_networks/get_info_funcs.py","file_url":"https://github.com/isadrtdinov/understanding-large-lrs/blob/HEAD/convolutional_networks/get_info_funcs.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":"8bc49ef2a0810046","mcp_get_code":{"code_sha256":"8bc49ef2a0810046"}},{"arxiv_id":"2209.03695","paper":"/paper/training-scale-invariant-neural-networks-on","title":"Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes","date":"2022-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tipt0p/three_regimes_on_the_sphere","path":"get_info_funcs.py","file_url":"https://github.com/tipt0p/three_regimes_on_the_sphere/blob/HEAD/get_info_funcs.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":"8bc49ef2a0810046","mcp_get_code":{"code_sha256":"8bc49ef2a0810046"}},{"arxiv_id":"2106.15739","paper":"/paper/on-the-periodic-behavior-of-neural-network","title":"On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay","date":"2021-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tipt0p/periodic_behavior_bn_wd","path":"get_info_funcs.py","file_url":"https://github.com/tipt0p/periodic_behavior_bn_wd/blob/HEAD/get_info_funcs.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":"8bc49ef2a0810046","mcp_get_code":{"code_sha256":"8bc49ef2a0810046"}},{"arxiv_id":"1905.04172","paper":"/paper/on-the-connection-between-adversarial","title":"On the Connection Between Adversarial Robustness and Saliency Map Interpretability","date":"2019-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cetmann/robustness-interpretability","path":"diff_ops.py","file_url":"https://github.com/cetmann/robustness-interpretability/blob/HEAD/diff_ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c91e29ab9169cee1","mcp_get_code":{"code_sha256":"c91e29ab9169cee1"}},{"arxiv_id":"1602.02068","paper":"/paper/from-softmax-to-sparsemax-a-sparse-model-of","title":"From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification","date":"2016-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AndreasMadsen/course-02456-sparsemax","path":"tensorflow_python/sparsemax.py","file_url":"https://github.com/AndreasMadsen/course-02456-sparsemax/blob/HEAD/tensorflow_python/sparsemax.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b95cf081a20568e0","mcp_get_code":{"code_sha256":"b95cf081a20568e0"}}]}