{"id":"W2962971773","doi":"10.1109/cvpr.2017.668","title":"Deep Pyramidal Residual Networks","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":687,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Residual; Convolutional neural network; Upsampling; Computer science; Benchmark (surveying); Artificial intelligence; Feature (linguistics); Deep learning; Dimension (graph theory); Pattern recognition (psychology); Generalization; Residual neural network; Pooling; Artificial neural network; Network architecture; Image (mathematics); Algorithm; Mathematics; Cartography","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":[0.0001571601,0.0003098227,0.0002977985,0.00006430243,0.0003907749,0.0006033842,0.00468662,0.0003211721,0.00002207001],"category_scores_gemma":[0.00003468544,0.000292595,0.0001167283,0.00009501901,0.0001258878,0.000289776,0.00609278,0.000921813,0.0001205994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005869801,"about_ca_system_score_gemma":0.00008673555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002892556,"about_ca_topic_score_gemma":0.00007005796,"domain_scores_codex":[0.9979111,0.00004309016,0.0003005816,0.001011631,0.0002810474,0.00045254],"domain_scores_gemma":[0.995428,0.0001164924,0.0003391693,0.00385378,0.0001034406,0.0001591023],"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":[0.000003698168,0.00004482769,0.0001316006,0.00001583199,0.00003505131,0.00002844688,0.00006573759,0.5490425,0.000009586151,0.2896966,0.01481157,0.1461146],"study_design_scores_gemma":[0.00009082182,0.00001041495,0.001196664,0.00002426427,0.000008040281,0.00001013641,0.000001360162,0.8765046,0.00005967604,0.1143558,0.007351295,0.000386888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001428336,0.0004606141,0.9740669,0.003006687,0.001072795,0.0004024385,0.000001457768,0.0007155617,0.02013075],"genre_scores_gemma":[0.4534211,0.0004890475,0.5363629,0.001175964,0.002126426,0.0003677949,0.0000537923,0.00006005829,0.005942896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4532783,"threshold_uncertainty_score":0.9999526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03010212868599168,"score_gpt":0.2971226287962158,"score_spread":0.2670205001102242,"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."}}