{"id":"W4320204347","doi":"10.48550/arxiv.2206.05860","title":"IGN : Implicit Generative Networks","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Discriminator; Reinforcement learning; Quantile regression; Computer science; Generator (circuit theory); Quantile; Baseline (sea); Bellman equation; Artificial intelligence; State (computer science); Function (biology); Generative grammar; Action (physics); Mathematical optimization; Machine learning; Econometrics; Algorithm; Mathematics; Power (physics)","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":[0.0008286976,0.0008797802,0.0008549779,0.0005673754,0.000483874,0.001166337,0.001986414,0.001328492,0.01214507],"category_scores_gemma":[0.0047812,0.0005588311,0.0007856266,0.0006687326,0.001237792,0.002059155,0.002340958,0.002736651,0.003057238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115428,"about_ca_system_score_gemma":0.0008999749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003324508,"about_ca_topic_score_gemma":0.006837448,"domain_scores_codex":[0.9995105,0.0001754957,0.00001505984,0.0001385917,0.000109108,0.00005123787],"domain_scores_gemma":[0.9989353,0.0006720135,0.00005148706,0.0001888559,0.00009137303,0.00006107166],"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":[0.0001103766,0.0000841186,0.001228876,0.0001338066,0.00006446381,0.0002022219,0.0001151961,0.6418924,0.001414135,0.2401836,0.01239327,0.1021776],"study_design_scores_gemma":[0.00001042944,0.000009300476,0.00006289924,0.00001146807,0.00000568896,0.00003358864,0.000004827836,0.8902267,0.0003118539,0.1060323,0.003285926,0.000005081797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009300726,0.0004894059,0.9725832,0.0008207975,0.0001355805,0.00005066032,0.0005977853,0.002904313,0.01311753],"genre_scores_gemma":[0.6933976,0.000849764,0.2681032,0.001026978,0.0002073072,0.0003484366,0.002641803,0.00148729,0.03193757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01214507,"threshold_uncertainty_score":0.04062933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06671544719590597,"score_gpt":0.1940389636844474,"score_spread":0.1273235164885415,"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."}}