{"id":"W2971133559","doi":"","title":"Universal Boosting Variational Inference","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Mathematical optimization; Boosting (machine learning); Hellinger distance; Degeneracy (biology); Applied mathematics; Algorithm; Computer science; Artificial intelligence","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":[0.003918097,0.001187501,0.002126199,0.001014978,0.0008343839,0.001526871,0.003027466,0.001548163,0.004363813],"category_scores_gemma":[0.011304,0.001042284,0.001425671,0.001088186,0.001399687,0.001861539,0.002883944,0.00260216,0.001429427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578566,"about_ca_system_score_gemma":0.001985455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003832948,"about_ca_topic_score_gemma":0.004957164,"domain_scores_codex":[0.9983463,0.0007099282,0.00006533255,0.0003253638,0.0004076841,0.0001452602],"domain_scores_gemma":[0.9972905,0.001424112,0.0001524816,0.000557465,0.0004171501,0.0001582588],"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":[0.00009530057,0.00006783153,0.001383862,0.0001937572,0.0001987068,0.00009457464,0.000124439,0.681635,0.002730043,0.1703802,0.007743443,0.1353529],"study_design_scores_gemma":[0.000004979447,0.000005787431,0.00005133375,0.000007158883,0.000006781509,0.00001373618,0.000003218556,0.9659372,0.0003903967,0.03256197,0.001013326,0.000004098317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001668862,0.00011127,0.9967725,0.00008630878,0.00002607552,0.00002732902,0.00004079564,0.0003661478,0.000900759],"genre_scores_gemma":[0.2719853,0.0004265674,0.7196659,0.0005534626,0.0002021095,0.0002861572,0.0006894519,0.000610442,0.005580735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004363813,"threshold_uncertainty_score":0.02072114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614696256608015,"score_gpt":0.2405896780365683,"score_spread":0.2244427154704881,"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."}}