{"id":"W4285241469","doi":"10.1109/tai.2022.3177394","title":"Nonoverlapping Feature Projection Convolutional Neural Network With Differentiable Loss Function","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Feature (linguistics); Differentiable function; Convolution (computer science); Computer science; Pattern recognition (psychology); Pixel; Artificial intelligence; Feature vector; Convolutional neural network; Projection (relational algebra); Separable space; Algorithm; Function (biology); Mathematics; Artificial neural network; Mathematical analysis","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":[0.0006291692,0.001261591,0.0009115448,0.0005449854,0.0002625876,0.0007958368,0.002214456,0.001255283,0.002856102],"category_scores_gemma":[0.001071384,0.0005086159,0.0006313984,0.0009434265,0.0007376879,0.002086028,0.001477814,0.001571699,0.001253469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009794496,"about_ca_system_score_gemma":0.001381834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00670945,"about_ca_topic_score_gemma":0.01127569,"domain_scores_codex":[0.9995515,0.00005534395,0.0000201136,0.000157871,0.0001459101,0.00006917637],"domain_scores_gemma":[0.9997106,0.0000593691,0.00003425158,0.00007664863,0.00009310748,0.00002601943],"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":[0.0002891676,0.0002286769,0.001135805,0.0001312288,0.0001518132,0.000207502,0.00006260903,0.3831078,0.02127485,0.01754135,0.01490226,0.560967],"study_design_scores_gemma":[0.00001265964,0.00003631536,0.0001676452,0.000004474828,0.000008283953,0.00003938553,0.000003725526,0.9917079,0.002821207,0.003844968,0.001346184,0.000007250746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01923901,0.0004975866,0.9740928,0.0002433167,0.00008006183,0.00004987425,0.0003140148,0.00297558,0.002507685],"genre_scores_gemma":[0.4622498,0.000694145,0.5160515,0.0005970895,0.0001085368,0.000292392,0.00251417,0.0003303169,0.01716203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00670945,"threshold_uncertainty_score":0.01334083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03097946788488648,"score_gpt":0.2552014065410654,"score_spread":0.224221938656179,"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."}}