{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005300557,0.001529558,0.002209551,0.001833291,0.00116618,0.002328194,0.003551252,0.002085983,0.006199391],"category_scores_gemma":[0.02389648,0.001515182,0.001869533,0.001911595,0.002568965,0.005160396,0.005841032,0.005472391,0.002122003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693976,"about_ca_system_score_gemma":0.003032367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496542,"about_ca_topic_score_gemma":0.00653902,"domain_scores_codex":[0.9967408,0.001223533,0.000146355,0.0008305946,0.0008865287,0.0001722033],"domain_scores_gemma":[0.9932842,0.003687618,0.0003492601,0.001692488,0.000687727,0.0002986827],"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":[0.0003069829,0.0001988306,0.002345149,0.0003894303,0.0002075489,0.0001879319,0.0003338298,0.3828391,0.004893172,0.3353884,0.01765472,0.2552549],"study_design_scores_gemma":[0.00002263877,0.00003488577,0.0001898959,0.00003564749,0.00001132427,0.00004495521,0.00002488394,0.813064,0.001338487,0.1809518,0.004265691,0.00001573656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002321386,0.000111351,0.9959918,0.0001609019,0.00002556006,0.00004395935,0.000150923,0.0004684899,0.0007257567],"genre_scores_gemma":[0.1299127,0.0005607191,0.8596399,0.0006465651,0.000230657,0.0004666507,0.002975253,0.001105097,0.004462454],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006199391,"threshold_uncertainty_score":0.0280323,"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."}}