{"id":"W4360764838","doi":"10.1109/icmla55696.2022.00097","title":"IGN : Implicit Generative Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Discriminator; Reinforcement learning; Computer science; Quantile regression; Generator (circuit theory); Baseline (sea); Quantile; Artificial intelligence; Bellman equation; State (computer science); Generative grammar; Action (physics); Machine learning; Mathematical optimization; Algorithm; Econometrics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001934121,0.00007085217,0.00007075515,0.00004307013,0.0003338027,0.00009730834,0.0007765502,0.0000132577,0.000460255],"category_scores_gemma":[0.000007582782,0.00006725382,0.00003409724,0.0003097274,0.00001119308,0.0001477748,0.000880727,0.0001851382,0.00003674317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006022288,"about_ca_system_score_gemma":0.00003121658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001260742,"about_ca_topic_score_gemma":6.749774e-7,"domain_scores_codex":[0.9991459,0.0000710989,0.0001225901,0.0002043329,0.0002406733,0.000215364],"domain_scores_gemma":[0.9994596,0.00004806583,0.00004762807,0.000377997,0.00002240377,0.00004433601],"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":[5.205118e-7,0.000004442516,0.0001275619,2.755238e-7,0.000005583719,0.000003828431,0.0001331491,0.8496602,0.00004285026,0.1404601,0.007962226,0.0015993],"study_design_scores_gemma":[0.00008756769,0.0000925885,0.0001944384,3.36568e-7,0.000001113167,0.000009488079,0.00003492279,0.9788647,0.0000733868,0.000346794,0.02020107,0.00009353833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000212949,0.00002302605,0.9663562,0.0006372082,0.0003926186,0.00008550274,1.552697e-7,0.0001846156,0.03210768],"genre_scores_gemma":[0.8904119,0.000004633173,0.08791815,0.004096965,0.0001073447,0.00005151128,0.000005353735,0.00001086587,0.01739325],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8901989,"threshold_uncertainty_score":0.503947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01386666570780476,"score_gpt":0.2332307319772495,"score_spread":0.2193640662694447,"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."}}