{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003864205,0.000296791,0.0002792923,0.0003611629,0.0002766181,0.0002842241,0.001800999,0.0002566206,0.00007841804],"category_scores_gemma":[0.00008515918,0.000367819,0.0001688434,0.0007806718,0.0000576632,0.0004974536,0.002230009,0.0008008604,0.0007136291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001955831,"about_ca_system_score_gemma":0.0002935201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002610002,"about_ca_topic_score_gemma":0.00004166276,"domain_scores_codex":[0.9979398,0.0001567851,0.0002467074,0.001089462,0.0001867271,0.0003804863],"domain_scores_gemma":[0.9980703,0.0003543057,0.0003118723,0.0008989323,0.0001776413,0.0001869567],"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":[0.000006434333,0.00002629729,0.002844494,0.00003692223,0.00005210808,0.0001360371,0.00055042,0.6096593,0.00007010234,0.3857647,0.0001268949,0.0007263093],"study_design_scores_gemma":[0.0002364194,0.00001594561,0.02040919,0.00009031768,0.00001990282,0.000004019678,0.00005701838,0.7380514,0.000009511687,0.2403212,0.0004017818,0.0003832656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04193746,0.00001032359,0.9531908,0.0002184662,0.0008177915,0.0001823,0.00001017495,0.0006869936,0.002945667],"genre_scores_gemma":[0.9891661,0.00004067752,0.007019753,0.0002376628,0.0001021922,0.000001659267,0.00004536882,0.00002551703,0.003361037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9472287,"threshold_uncertainty_score":0.9998774,"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."}}