{"id":"W1839876751","doi":"10.48550/arxiv.1510.08345","title":"A polynomial expansion line search for large-scale unconstrained minimization of smooth L2-regularized loss functions, with implementation in Apache Spark","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Line search; Conjugate gradient method; Broyden–Fletcher–Goldfarb–Shanno algorithm; Computer science; Gradient descent; Algorithm; Polynomial; Mathematical optimization; Taylor series; Line (geometry); Function (biology); Mathematics; Artificial intelligence; 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":[0.001162891,0.00133296,0.001036055,0.0006600067,0.000591937,0.0008295407,0.001606081,0.001280101,0.008958397],"category_scores_gemma":[0.004397538,0.0006106395,0.0008551445,0.001072498,0.0006527752,0.001001865,0.001320371,0.001682431,0.004127372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007781909,"about_ca_system_score_gemma":0.002958804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103181,"about_ca_topic_score_gemma":0.01670432,"domain_scores_codex":[0.9993334,0.0001397319,0.00003850018,0.0001318732,0.0002696219,0.00008690004],"domain_scores_gemma":[0.9991511,0.0003967543,0.00005603296,0.00009564673,0.0002291194,0.00007137036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005169553,0.0003198036,0.00184973,0.0004535029,0.0001559315,0.0004058488,0.000295778,0.5862246,0.01534669,0.01867474,0.03776452,0.3379921],"study_design_scores_gemma":[0.00007584767,0.00003739462,0.0001093553,0.000007975917,0.000005710664,0.00003524339,0.00002121923,0.9925458,0.001804251,0.002593349,0.002753167,0.00001070251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009575013,0.0001984752,0.9749921,0.000170155,0.00005873729,0.0001194027,0.0002679223,0.01236561,0.002252538],"genre_scores_gemma":[0.09978524,0.00009830948,0.8938463,0.0001593078,0.00002564372,0.0004526719,0.0009201838,0.001973083,0.00273924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01103181,"threshold_uncertainty_score":0.02996886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101741879369255,"score_gpt":0.2958325813646479,"score_spread":0.1856583934277224,"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."}}