{"id":"W4294237904","doi":"10.1145/3558774","title":"AutoML Loss Landscapes","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oracle (Canada); University of British Columbia","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Set (abstract data type); Convexity; Bayesian probability; Biology","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":[0.008143367,0.001990803,0.00183015,0.002647149,0.001132484,0.004159973,0.002298668,0.002922827,0.005469622],"category_scores_gemma":[0.03614955,0.0006774479,0.00107878,0.001150221,0.002348041,0.003800753,0.003382997,0.003328017,0.001185149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002627954,"about_ca_system_score_gemma":0.001126518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341265,"about_ca_topic_score_gemma":0.001239792,"domain_scores_codex":[0.9957874,0.001915902,0.0002210226,0.0006129192,0.001131011,0.0003316067],"domain_scores_gemma":[0.982062,0.01311958,0.00105033,0.00151176,0.00172879,0.0005274515],"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":[0.0003763084,0.0002722792,0.007389782,0.0006186503,0.0002205344,0.0003489233,0.0002424306,0.7273287,0.003482257,0.1357947,0.02149707,0.1024284],"study_design_scores_gemma":[0.00002999068,0.0001942411,0.001899333,0.0001360083,0.0000318866,0.0002779978,0.0001173896,0.8878391,0.001417478,0.1016996,0.006311655,0.00004534672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1363405,0.005587509,0.8120055,0.00472759,0.0002404303,0.0003920779,0.001500054,0.00183626,0.03737005],"genre_scores_gemma":[0.7802649,0.001755595,0.1999508,0.00211926,0.0001874443,0.001043742,0.003072708,0.001289683,0.010316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008143367,"threshold_uncertainty_score":0.04306674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008214584684334612,"score_gpt":0.2314862547169162,"score_spread":0.2232716700325816,"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."}}