{"id":"W2531425418","doi":"10.48550/arxiv.1610.02915","title":"Deep Pyramidal Residual Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science, ICT and Future Planning","keywords":"Residual; Computer science; Upsampling; Convolutional neural network; Benchmark (surveying); Feature (linguistics); Artificial intelligence; Code (set theory); Deep learning; Dimension (graph theory); Generalization; Pooling; Residual neural network; Pattern recognition (psychology); Network architecture; Artificial neural network; Image (mathematics); Algorithm; Cartography; Mathematics; Geography; Set (abstract data type)","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":[0.0004754091,0.0008945948,0.0007326772,0.0004572107,0.0002704678,0.0007231213,0.001710661,0.0008005541,0.005522468],"category_scores_gemma":[0.001638386,0.0003179983,0.0006158947,0.0006501287,0.0005107037,0.001448337,0.001171218,0.001326568,0.002465605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007049862,"about_ca_system_score_gemma":0.0008839761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004806695,"about_ca_topic_score_gemma":0.007281928,"domain_scores_codex":[0.9996506,0.00003580724,0.00001910003,0.0001168874,0.0001106801,0.00006693208],"domain_scores_gemma":[0.9996358,0.00006758198,0.00004906441,0.00009913646,0.0001161316,0.00003221929],"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":[0.0002751482,0.0001246836,0.001269991,0.0002969432,0.0001619674,0.0001654911,0.00006457578,0.4120791,0.02932309,0.04828616,0.0230482,0.4849047],"study_design_scores_gemma":[0.00002206666,0.00008015798,0.0003277019,0.00001958897,0.00002905002,0.00007422272,0.0000106604,0.9675763,0.008123856,0.01642676,0.007295337,0.00001423336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02928485,0.001718629,0.9505649,0.0004875362,0.0001825538,0.00008133589,0.0008912148,0.00505519,0.01173383],"genre_scores_gemma":[0.5947997,0.002020688,0.3723369,0.0006168596,0.0001947765,0.0001866902,0.004558098,0.0004699133,0.02481635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005522468,"threshold_uncertainty_score":0.01847446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05151344467729533,"score_gpt":0.1939313041761768,"score_spread":0.1424178594988814,"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."}}