{"id":"W4386103321","doi":"10.48550/arxiv.2308.10014","title":"Semi-Implicit Variational Inference via Score Matching","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Peking University; National Natural Science Foundation of China; Institute for Catastrophic Loss Reduction","keywords":"Inference; Matching (statistics); Minimax; Bayesian inference; Computer science; Bayesian probability; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004098054,0.001141792,0.00206149,0.001198915,0.0008106314,0.00188324,0.003792993,0.00236969,0.003940171],"category_scores_gemma":[0.01528609,0.001287816,0.001413173,0.001239062,0.002210002,0.003160004,0.003331119,0.003606441,0.00109423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408798,"about_ca_system_score_gemma":0.002375523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004719917,"about_ca_topic_score_gemma":0.006269719,"domain_scores_codex":[0.9980095,0.0009758867,0.00007885279,0.000377582,0.0004273182,0.0001307998],"domain_scores_gemma":[0.9954767,0.002870956,0.0003030022,0.0007144766,0.0004245991,0.0002102959],"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":[0.00009375275,0.00007934053,0.0009797803,0.0001326093,0.0001138453,0.00008050216,0.000152108,0.7613881,0.00217363,0.1520649,0.003186299,0.07955512],"study_design_scores_gemma":[0.000004623505,0.000005358461,0.00003027717,0.000004564922,0.000002806649,0.000007691762,0.000003793356,0.9716261,0.0002241851,0.02778138,0.0003053209,0.000003920984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003019272,0.0000725205,0.9957991,0.00009860389,0.00001171117,0.00002273363,0.00003795447,0.000221485,0.0007165603],"genre_scores_gemma":[0.3474047,0.0003203284,0.6441497,0.0003829872,0.0001412434,0.0003322377,0.0008004123,0.0006259375,0.005842506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004719917,"threshold_uncertainty_score":0.02167284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170431977123342,"score_gpt":0.2177362357350303,"score_spread":0.1006930380226961,"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."}}