{"id":"W4375869437","doi":"10.1109/icassp49357.2023.10094582","title":"Performing Neural Architecture Search Without Gradients","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Architecture; Kernel (algebra); Ranking (information retrieval); Process (computing); Artificial intelligence; Code (set theory); Beam search; Margin (machine learning); Machine learning; Gaussian process; Search algorithm; Gaussian; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006583435,0.001206179,0.001029113,0.0007138117,0.000478637,0.001071408,0.001442674,0.001193286,0.01079647],"category_scores_gemma":[0.004062977,0.0005155,0.0006992429,0.0005923579,0.000531979,0.001419709,0.001499526,0.001062703,0.002530461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007825997,"about_ca_system_score_gemma":0.00181388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004989738,"about_ca_topic_score_gemma":0.01209662,"domain_scores_codex":[0.9994641,0.0001092756,0.00002961654,0.000142935,0.0001393436,0.0001145821],"domain_scores_gemma":[0.9992569,0.0003047152,0.00004285978,0.0001594724,0.0001863734,0.00004962839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002854115,0.0001390286,0.003331484,0.0002797116,0.0001186634,0.000192461,0.0001198044,0.4547913,0.01531216,0.01702718,0.0170201,0.4913827],"study_design_scores_gemma":[0.00004044123,0.000080486,0.0003907805,0.00001778111,0.00002034194,0.00005807088,0.00004330064,0.9788345,0.003427813,0.01481545,0.002260608,0.00001029114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1209887,0.001021564,0.8514902,0.0005308476,0.0001756125,0.0001850872,0.0004444182,0.009682938,0.01548079],"genre_scores_gemma":[0.6204017,0.0001982882,0.3654828,0.0003577151,0.00005337363,0.0002080843,0.001333846,0.001098221,0.01086594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01079647,"threshold_uncertainty_score":0.03611773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050067906354909,"score_gpt":0.298351156787732,"score_spread":0.2678504777241829,"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."}}