{"id":"W4385764544","doi":"10.24963/ijcai.2023/101","title":"GeNAS: Neural Architecture Search with Better Generalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Architecture; Generalization; Artificial neural network; Artificial intelligence; Network architecture; Segmentation; Measure (data warehouse); Flatness (cosmology); Pattern recognition (psychology); Machine learning; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005207537,0.00008952694,0.00006381978,0.00009389303,0.0001287999,0.00007129399,0.0004694846,0.00002346866,0.00001517516],"category_scores_gemma":[0.000002680119,0.0000638889,0.00001963159,0.001333228,0.00002393454,0.0002038974,0.0001751456,0.0001039016,0.0001786445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001186642,"about_ca_system_score_gemma":0.0000137498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005364731,"about_ca_topic_score_gemma":0.00001467133,"domain_scores_codex":[0.9990831,0.00003043518,0.0000888632,0.0003144344,0.0002154411,0.0002677229],"domain_scores_gemma":[0.9993697,0.00004746168,0.00001956476,0.0004540931,0.00004250404,0.0000666537],"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":[0.000009067897,0.00002991832,0.002897927,0.00001429542,0.00001910262,0.00004344923,0.0006567297,0.6410446,0.01028264,0.119705,0.01467044,0.2106268],"study_design_scores_gemma":[0.0002234026,0.00006520349,0.007482269,0.000004563869,0.000002519987,0.00003747891,0.000009690069,0.9689752,0.005372424,0.007053185,0.01053579,0.0002382553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06427999,0.000008466655,0.9245795,0.009388351,0.00004048262,0.0001743181,7.779679e-7,0.0007327142,0.0007953784],"genre_scores_gemma":[0.7349062,0.00001519982,0.2570487,0.004454013,0.0002569619,0.0001060971,0.00003528755,0.00002998662,0.003147537],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6706262,"threshold_uncertainty_score":0.2605314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207507601510287,"score_gpt":0.2667131147926719,"score_spread":0.2459623546416432,"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."}}