{"id":"W2171009797","doi":"10.1109/icassp.2004.1327157","title":"Theory of Monte Carlo sampling-based Alopex algorithms for neural networks","year":2004,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Monte Carlo method; Computer science; Algorithm; Convergence (economics); Sampling (signal processing); Artificial neural network; Hybrid Monte Carlo; Monte Carlo integration; Importance sampling; Artificial intelligence; Bayesian probability; Mathematical optimization; Markov chain Monte Carlo; Mathematics; Statistics","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":[0.003951209,0.0009868771,0.001199758,0.001303792,0.000758429,0.001480559,0.002429956,0.001755467,0.004273712],"category_scores_gemma":[0.01713182,0.0008001987,0.0007278142,0.001402687,0.001862745,0.002999927,0.00224995,0.002338615,0.001023922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132031,"about_ca_system_score_gemma":0.001343258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002042689,"about_ca_topic_score_gemma":0.001896074,"domain_scores_codex":[0.9983271,0.000875674,0.00008117827,0.0001987507,0.0004293884,0.00008789751],"domain_scores_gemma":[0.9937052,0.00480334,0.000252599,0.0004490988,0.000667998,0.0001218551],"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":[0.0001179615,0.00005943857,0.0007301422,0.0001454681,0.00006306167,0.00006094882,0.0001192451,0.5961343,0.00151763,0.284478,0.002202974,0.1143707],"study_design_scores_gemma":[0.00001390226,0.0000224214,0.00006991034,0.00001235048,0.000004456968,0.00002293876,0.000004199082,0.9390453,0.0005340113,0.05896377,0.001298334,0.000008367143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008626673,0.0001057243,0.9983329,0.00005453462,0.0000147542,0.00002143323,0.00001204763,0.0001033948,0.0004925881],"genre_scores_gemma":[0.1127609,0.0005597159,0.8820155,0.0002314966,0.0001359599,0.0008055575,0.0001836273,0.0001854177,0.003121916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004273712,"threshold_uncertainty_score":0.02089626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271876528672238,"score_gpt":0.2796618321461932,"score_spread":0.2524741792789694,"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."}}