{"id":"W4389510032","doi":"10.1049/cvi2.12260","title":"Deep network with double reuses and convolutional shortcuts","year":2023,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Feature (linguistics); Reuse; Benchmark (surveying); Convolutional code; Artificial intelligence; Pascal (unit); Convolution (computer science); Pattern recognition (psychology); Deep learning; Algorithm; Decoding methods; Artificial neural network; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001211567,0.0001440477,0.0001386049,0.00006769463,0.0002725811,0.0001340774,0.0004737902,0.00004520738,0.000004514246],"category_scores_gemma":[0.000001245521,0.0001195149,0.00002582448,0.0007747924,0.00007271482,0.0004318517,0.0006025773,0.0001195154,0.0001265046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001936009,"about_ca_system_score_gemma":0.0000213435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001864317,"about_ca_topic_score_gemma":0.000008341304,"domain_scores_codex":[0.9987184,0.00002676317,0.0001647168,0.0005034661,0.0002528371,0.0003338041],"domain_scores_gemma":[0.9990886,0.0001616035,0.0000614828,0.0005030012,0.00007060957,0.0001147446],"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":[0.0001204925,0.0001029531,0.004711855,0.00002741105,0.00005302735,0.0001235877,0.0003855113,0.5745026,0.0002754311,0.1355485,0.1210218,0.1631269],"study_design_scores_gemma":[0.0004698275,0.0002010136,0.02422409,0.00003662348,0.000003924862,0.00006806713,0.000001685509,0.9439453,0.00003826294,0.008864273,0.02193934,0.0002075608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02571477,0.0001276242,0.9711569,0.001573067,0.0002699512,0.0002328405,7.112478e-7,0.0007385943,0.0001855594],"genre_scores_gemma":[0.5631099,0.0001250771,0.4347483,0.0009496689,0.0007139862,0.00005443598,0.00002897185,0.00002666446,0.0002430039],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5373951,"threshold_uncertainty_score":0.4873677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162655516393827,"score_gpt":0.2693600447507253,"score_spread":0.2530944931113426,"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."}}