{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002731839,0.001212222,0.001636008,0.0008865416,0.0004562511,0.001343139,0.002007972,0.001738639,0.001988339],"category_scores_gemma":[0.00738201,0.0008298443,0.0008993042,0.0008858863,0.001123683,0.001718195,0.001223418,0.00240502,0.0006554871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130059,"about_ca_system_score_gemma":0.001631721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003579758,"about_ca_topic_score_gemma":0.004206594,"domain_scores_codex":[0.9992767,0.0002695757,0.00004370489,0.000142202,0.0002008877,0.00006700282],"domain_scores_gemma":[0.9969445,0.002086512,0.0002373081,0.0002967644,0.0003291936,0.0001056919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007237403,0.0000579359,0.0005648008,0.00009359905,0.0000829819,0.00004816802,0.00004015307,0.9378581,0.0008599885,0.01564814,0.001674185,0.04299956],"study_design_scores_gemma":[0.000005349731,0.00001033963,0.00003179809,0.000005731186,0.000003534904,0.000005599261,0.000002031125,0.9960138,0.0001886351,0.003408651,0.0003222182,0.000002371526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009488334,0.0008161665,0.9871884,0.0003171602,0.00007351693,0.00003309499,0.00004778098,0.0006671224,0.00136845],"genre_scores_gemma":[0.4815952,0.0007566421,0.5114313,0.0004345407,0.0001996819,0.0003217619,0.0003498748,0.000406141,0.004504925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003579758,"threshold_uncertainty_score":0.01444757,"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."}}