{"id":"W24640360","doi":"10.1007/978-3-319-06483-3_6","title":"Analysis of Feature Maps Selection in Supervised Learning Using Convolutional Neural Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Kernel (algebra); Field (mathematics); Deep learning; Receptive field; Feature selection; Object (grammar); Machine learning; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006117419,0.0003510452,0.0006264973,0.001331879,0.000222096,0.0002002475,0.001443182,0.0003089906,0.00001179526],"category_scores_gemma":[0.00002757185,0.0003388553,0.0002048247,0.0031088,0.0003245582,0.0002668168,0.0005098593,0.001079834,0.000001218642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002096904,"about_ca_system_score_gemma":0.0001506067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006199918,"about_ca_topic_score_gemma":0.0002627459,"domain_scores_codex":[0.997282,0.00007388121,0.0004793512,0.001083612,0.0005826855,0.0004984163],"domain_scores_gemma":[0.9984601,0.0004005484,0.0003424355,0.0004800885,0.0002171574,0.00009968639],"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":[0.000003227045,0.000009886192,0.001843348,0.00000717631,0.00002368562,0.000003699152,0.00006266583,0.9058996,0.0001332598,0.005355177,0.000005353627,0.08665296],"study_design_scores_gemma":[0.000144483,0.00005339608,0.002238867,0.00008494171,0.00004612785,0.00001515095,7.10751e-8,0.9927657,0.00003086215,0.00416169,0.0001437253,0.0003150155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001830033,0.0002391676,0.996906,0.0002902746,0.0003325796,0.0002181784,0.000002561622,0.00006219777,0.0001189669],"genre_scores_gemma":[0.9102894,0.00002154721,0.08878908,0.0004192449,0.0003336712,0.000006086439,0.00002320514,0.00002014013,0.00009759996],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9084594,"threshold_uncertainty_score":0.9999064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629056470674845,"score_gpt":0.243130483783615,"score_spread":0.2268399190768666,"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."}}