{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030078,0.0003691861,0.0003489095,0.0002396014,0.0003957544,0.0002235152,0.003235514,0.0002407312,0.0002741279],"category_scores_gemma":[0.00002601188,0.000458955,0.0002423663,0.0007225406,0.00008104456,0.0003077103,0.00688204,0.001324218,0.00007044527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004586044,"about_ca_system_score_gemma":0.000226201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008227465,"about_ca_topic_score_gemma":0.000006579219,"domain_scores_codex":[0.9976237,0.0002649862,0.0002409039,0.001218393,0.0001478629,0.0005041428],"domain_scores_gemma":[0.9974775,0.0001324397,0.0003626812,0.001741854,0.0001157885,0.0001697302],"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":[0.000006102779,0.00001716209,0.0007899936,0.00001161078,0.00007802133,0.0001840864,0.0001537807,0.8352281,0.000003930457,0.1624066,0.0009738806,0.0001466907],"study_design_scores_gemma":[0.0002357294,0.00007666301,0.0003668373,0.00001779734,0.00003898174,0.000003600122,0.00005477464,0.9914877,0.00001174971,0.004557766,0.002674448,0.0004739626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004699784,0.00005607702,0.9817442,0.00009782088,0.001309296,0.0003299667,0.000004631263,0.0003872502,0.01137097],"genre_scores_gemma":[0.9846776,0.0001745866,0.005091136,0.0003485279,0.0001534695,0.000002884161,0.00004481755,0.0000295296,0.009477407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9799778,"threshold_uncertainty_score":0.9997862,"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."}}