{"id":"W3162914052","doi":"10.24963/ijcai.2021/307","title":"Generative Adversarial Neural Architecture Search","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Discriminator; Reinforcement learning; Artificial intelligence; Machine learning; Generator (circuit theory); Space (punctuation); Architecture; Stability (learning theory); Adversarial system; Generative grammar; Convergence (economics)","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":[0.00009382113,0.0003017717,0.0002921249,0.00009178666,0.0001679048,0.0003818686,0.001834415,0.0002023769,0.00006744524],"category_scores_gemma":[0.00002112221,0.0002738842,0.0001752485,0.0004001234,0.00005402156,0.0001735046,0.004786174,0.001254422,0.00002864765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007544238,"about_ca_system_score_gemma":0.0002504785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003558881,"about_ca_topic_score_gemma":0.0000503964,"domain_scores_codex":[0.9976458,0.0001740553,0.0002586022,0.001126646,0.0003999624,0.0003949681],"domain_scores_gemma":[0.9978817,0.000141784,0.00008818523,0.001561411,0.0001707261,0.0001562529],"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":[0.000008335976,0.00007965775,0.00002848644,0.00003309886,0.00006981275,0.00008589602,0.001418215,0.8321521,0.002335194,0.07194795,0.002220542,0.08962066],"study_design_scores_gemma":[0.000342512,0.00004596759,0.0003024463,0.00003439252,0.00001597838,0.00006045754,0.00005164828,0.9594508,0.01038281,0.02487371,0.003636349,0.0008029697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005044526,0.0002082311,0.9830187,0.007028275,0.0009683375,0.0004827974,0.00000778273,0.00038687,0.002854507],"genre_scores_gemma":[0.2933405,0.00004870666,0.7025474,0.001554238,0.0009750238,0.0001599876,0.00009426656,0.0000274957,0.001252427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2882959,"threshold_uncertainty_score":0.9999713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0315746964924969,"score_gpt":0.2958185963586319,"score_spread":0.2642438998661351,"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."}}