{"id":"W2618421303","doi":"10.48550/arxiv.1705.09279","title":"Filtering Variational Objectives","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; European Commission","keywords":"Estimator; Variance (accounting); Upper and lower bounds; Computer science; Particle filter; Latent variable; Maximum likelihood; Filter (signal processing); Mathematics; Applied mathematics; Mathematical optimization; Statistics; Economics","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":[0.006327707,0.001740582,0.001875703,0.001451744,0.0006920145,0.002916197,0.002041015,0.003200621,0.004730273],"category_scores_gemma":[0.02816723,0.00101945,0.001114514,0.001222715,0.002072859,0.003332191,0.002983992,0.003847121,0.0008090163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001972677,"about_ca_system_score_gemma":0.002142872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003798441,"about_ca_topic_score_gemma":0.003932321,"domain_scores_codex":[0.9974014,0.001258441,0.0001278077,0.0004577372,0.0005815895,0.0001731747],"domain_scores_gemma":[0.9908476,0.007503926,0.0004552811,0.0004718488,0.0005375602,0.0001838606],"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":[0.00007277836,0.00005752857,0.0008115859,0.0002731037,0.0001153793,0.00004717995,0.0001145577,0.5807774,0.001162082,0.3483551,0.004377706,0.06383565],"study_design_scores_gemma":[0.000008573381,0.00002369947,0.0001034443,0.00005734749,0.00001115348,0.00001701955,0.000008970743,0.9009948,0.0004504432,0.09619293,0.002121789,0.000009757076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001807676,0.0002978447,0.995698,0.0002459485,0.00003346238,0.00001513817,0.00005980351,0.00009455106,0.001747677],"genre_scores_gemma":[0.3032151,0.001694621,0.6820673,0.0008584547,0.0003686204,0.0005295501,0.001029909,0.0006246284,0.009611784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006327707,"threshold_uncertainty_score":0.03346449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06642101412482661,"score_gpt":0.1953326983003119,"score_spread":0.1289116841754853,"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."}}