{"id":"W2891678505","doi":"10.48550/arxiv.1809.01818","title":"Improving Explorability in Variational Inference with Annealed Variational Objectives","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Inference; Robustness (evolution); Computer science; Mathematical optimization; Artificial intelligence; Machine learning; Algorithm; Mathematics","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":[0.003516234,0.001114971,0.001237578,0.0006475726,0.0005472513,0.001006242,0.001354106,0.001492892,0.001707446],"category_scores_gemma":[0.01204295,0.0008742607,0.0009284057,0.0004925012,0.001825462,0.002168182,0.002756869,0.002737537,0.0003001121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004499,"about_ca_system_score_gemma":0.0009459782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002705844,"about_ca_topic_score_gemma":0.004474206,"domain_scores_codex":[0.9990853,0.0004800763,0.00004059922,0.0001632743,0.0001553973,0.00007528526],"domain_scores_gemma":[0.9952322,0.003863634,0.000223411,0.0003787699,0.000174631,0.0001274145],"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":[0.00009775009,0.0000518602,0.0009747106,0.00007090085,0.00006910766,0.00007472008,0.0001632289,0.9116822,0.003653298,0.05110763,0.0006720579,0.03138259],"study_design_scores_gemma":[0.000005579622,0.00001294041,0.00004051227,0.000004752057,0.000003737748,0.000008728342,0.000003490306,0.9872572,0.0004716437,0.01206531,0.0001225283,0.000003535627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02116823,0.000164661,0.9772864,0.0001607616,0.00001561636,0.00002197254,0.00002337194,0.0002341371,0.0009247687],"genre_scores_gemma":[0.7375591,0.0002238472,0.2590034,0.0002616223,0.00006628077,0.0001488972,0.0001453019,0.0002791065,0.002312575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003516234,"threshold_uncertainty_score":0.01859581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04073361515774715,"score_gpt":0.1796875463963157,"score_spread":0.1389539312385685,"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."}}