{"id":"W2800780290","doi":"10.1109/cvprw.2018.00217","title":"Highway Network Block with Gates Constraints for Training Very Deep Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds De La Recherche Scientifique - FNRS; Canadian Institute for Advanced Research","keywords":"MNIST database; Generalization; Computer science; Block (permutation group theory); Artificial intelligence; Layer (electronics); Feature (linguistics); Deep learning; Reduction (mathematics); Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006398613,0.001267559,0.0007491519,0.0004616026,0.0003198308,0.0005479888,0.002150997,0.001189341,0.00705571],"category_scores_gemma":[0.00230173,0.0005286373,0.0005597168,0.0006391996,0.0006827091,0.001976191,0.001421305,0.002071927,0.001544757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009976821,"about_ca_system_score_gemma":0.001623796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005987784,"about_ca_topic_score_gemma":0.01432397,"domain_scores_codex":[0.9996058,0.00009984052,0.00002501463,0.00009738886,0.0001044484,0.00006746715],"domain_scores_gemma":[0.9994802,0.000213993,0.00005532586,0.0001189153,0.00009665052,0.00003506043],"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":[0.0001723937,0.0001529971,0.001182065,0.0001692706,0.00006348683,0.0001409487,0.0000549087,0.747322,0.009999938,0.02579162,0.008667547,0.2062829],"study_design_scores_gemma":[0.00001278596,0.00006526642,0.0001021493,0.00001033195,0.000008038812,0.00001807957,0.000007312891,0.9897859,0.002216183,0.005772362,0.001995817,0.000005746253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03421661,0.0004316806,0.9576218,0.0002828135,0.00007208502,0.0001369727,0.000471374,0.002884709,0.003881996],"genre_scores_gemma":[0.5682282,0.0004793888,0.4173396,0.0005989766,0.0001052869,0.0008011662,0.0036535,0.0005276099,0.008266295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00705571,"threshold_uncertainty_score":0.02360368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008039840589761541,"score_gpt":0.1970276794124623,"score_spread":0.1889878388227007,"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."}}