{"id":"W2948340529","doi":"10.48550/arxiv.1906.03329","title":"Sparse Variational Inference: Bayesian Coresets from Scratch","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Inference; Scalability; Automatic summarization; Bayesian inference; Bayesian probability; Approximate inference; Machine learning; Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Database","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"],"consensus_categories":[],"category_scores_codex":[0.0001927327,0.0004873013,0.0004946026,0.0002786345,0.0001819701,0.0004947326,0.003546824,0.00052901,0.0004121457],"category_scores_gemma":[0.00007267185,0.0005407643,0.0002112432,0.0007029303,0.00009317847,0.0007809871,0.00286073,0.0009013822,0.0007276344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001961929,"about_ca_system_score_gemma":0.001152799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006253566,"about_ca_topic_score_gemma":0.0001096795,"domain_scores_codex":[0.9970335,0.0001380332,0.0003280268,0.00173551,0.0002453071,0.000519652],"domain_scores_gemma":[0.9968275,0.0002491161,0.0004387946,0.001883385,0.0003204579,0.0002807135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004027643,0.0002037009,0.02836734,0.0001264677,0.0001904186,0.0003886017,0.0004171933,0.1227834,0.0000338401,0.8438778,0.002313715,0.001257271],"study_design_scores_gemma":[0.0003749448,0.00003664179,0.009218764,0.0001311165,0.00003974676,9.952612e-7,0.00001888384,0.590707,0.00003947037,0.3986665,0.0002377605,0.0005281754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03065425,0.0000453536,0.9609345,0.0004715702,0.00117413,0.000311331,0.0001323794,0.0002637046,0.006012835],"genre_scores_gemma":[0.9865053,0.0001210721,0.01147438,0.0003659163,0.0001572058,0.00000184931,0.0001160888,0.00002233037,0.001235876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.955851,"threshold_uncertainty_score":0.9997044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05448680894328504,"score_gpt":0.2006955048921324,"score_spread":0.1462086959488474,"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."}}