{"id":"W2903574704","doi":"10.1609/aaai.v33i01.33013943","title":"Meta-Descent for Online, Continual Prediction","year":2019,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Stochastic gradient descent; Computer science; Gradient descent; Range (aeronautics); Mathematical optimization; Descent (aeronautics); Hessian matrix; Artificial intelligence; Algorithm; Mathematics; Applied mathematics; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003911413,0.0005879289,0.001365017,0.0005219295,0.0002484455,0.000749977,0.00497557,0.000463674,0.0004963198],"category_scores_gemma":[0.008964201,0.0003661696,0.001054994,0.0007940918,0.0006503559,0.0003769608,0.002051849,0.001373971,0.0002115017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659041,"about_ca_system_score_gemma":0.0005016752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004064166,"about_ca_topic_score_gemma":0.0000289064,"domain_scores_codex":[0.9921551,0.00007572273,0.002118642,0.001639766,0.00332633,0.0006844362],"domain_scores_gemma":[0.9889662,0.001420664,0.001942839,0.001018676,0.006472613,0.0001790517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003716139,0.003042888,0.001194087,0.001058694,0.003299178,0.000002289753,0.004419466,0.03718725,0.02490877,0.3230666,0.01961441,0.5784902],"study_design_scores_gemma":[0.00008243765,0.0004641557,0.0003141534,0.0003320431,0.0004660518,0.000002649839,0.001535937,0.181286,0.1583851,0.6540527,0.002603833,0.0004749222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2898013,0.001023976,0.6149903,0.02789283,0.01895594,0.02374913,0.009547911,0.0006231183,0.01341551],"genre_scores_gemma":[0.9871867,0.0001322695,0.006116076,0.000147526,0.000507541,0.0003554174,0.00002026588,0.00005751358,0.005476735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6973854,"threshold_uncertainty_score":0.999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5329035804286594,"score_gpt":0.4718520267375428,"score_spread":0.06105155369111653,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}