{"id":"W2103431968","doi":"10.1007/3-540-45665-1_2","title":"Scaling Large Learning Problems with Hard Parallel Mixtures","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Parallelizable manifold; Probabilistic logic; Generalization; Quadratic equation; Support vector machine; Algorithm; Theoretical computer science; Machine learning; Artificial intelligence; Mathematics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000673744,0.0007390302,0.0006572204,0.0006785502,0.0005616981,0.001324837,0.00360085,0.0003699191,0.00007018474],"category_scores_gemma":[0.00007514079,0.0005748877,0.0001244066,0.0008219719,0.000522913,0.0008976571,0.001088206,0.001580337,0.00009133724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001615622,"about_ca_system_score_gemma":0.0004018076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001259372,"about_ca_topic_score_gemma":0.00003226027,"domain_scores_codex":[0.9949847,0.00003881003,0.0005482124,0.002011972,0.001260023,0.001156287],"domain_scores_gemma":[0.9975678,0.000270709,0.0004259003,0.001175727,0.0002990826,0.0002607746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001580483,0.0001239921,0.0008039773,0.0003918103,0.00005124748,0.0004928472,0.003524393,0.1756078,0.00009538002,0.1032241,0.00008003683,0.7155886],"study_design_scores_gemma":[0.0007399603,0.0005369532,0.0002632093,0.001983844,0.00001949357,0.0003250256,4.509447e-7,0.8438029,0.0003710179,0.1411194,0.009106302,0.001731421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003711351,0.0008929455,0.9904652,0.000809534,0.0004879231,0.000374899,0.000002770624,0.0003107624,0.006618819],"genre_scores_gemma":[0.2364402,0.0002239717,0.7591749,0.001359328,0.0004720408,0.00002505073,0.000006898186,0.00007875132,0.00221894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7138572,"threshold_uncertainty_score":0.9997119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503603747282623,"score_gpt":0.218231011784099,"score_spread":0.2031949743112728,"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."}}