{"id":"W4372349723","doi":"10.1109/icassp49357.2023.10095893","title":"Reducing the Computational Complexity of Learning with Random Convolutional Features","year":2023,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Feature extraction; Feature selection; Simple random sample; Feature (linguistics); Computational complexity theory; Scalability; Artificial intelligence; Pattern recognition (psychology); Machine learning; Data mining; Algorithm","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":[0.001015954,0.001099325,0.0008263415,0.0007257091,0.0003452644,0.0007301021,0.00142994,0.0006729176,0.00449426],"category_scores_gemma":[0.007213546,0.0004867755,0.0007308564,0.0007437422,0.000550742,0.00217163,0.001072552,0.001165082,0.001331589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00099606,"about_ca_system_score_gemma":0.001546366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009106521,"about_ca_topic_score_gemma":0.01248343,"domain_scores_codex":[0.9991475,0.0001942516,0.00005861451,0.0001620045,0.0003113055,0.0001262458],"domain_scores_gemma":[0.9973558,0.001740116,0.0001751192,0.0004197207,0.0002593994,0.00004978071],"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":[0.000417374,0.0001639748,0.002672484,0.0001945432,0.0001192729,0.0001905578,0.00006394134,0.3786784,0.01502978,0.01429342,0.005444214,0.582732],"study_design_scores_gemma":[0.00001978008,0.00004121201,0.0004145985,0.000006846889,0.00001414717,0.00004051585,0.0000101111,0.9909168,0.003236439,0.00443674,0.0008562263,0.000006577259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04980941,0.0005337128,0.9425229,0.0005141344,0.0000659472,0.0001071109,0.0001721955,0.004020985,0.002253522],"genre_scores_gemma":[0.5734704,0.0004498889,0.4201154,0.0003101522,0.00008531311,0.0003052143,0.0008031998,0.0002963562,0.004164155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009106521,"threshold_uncertainty_score":0.018107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981114836972058,"score_gpt":0.240813769614991,"score_spread":0.2110026212452704,"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."}}