{"id":"W3010200124","doi":"10.1109/wacv45572.2020.9093431","title":"ADNet: Adaptively Dense Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Fuse (electrical); Benchmark (surveying); Reuse; Convolutional neural network; Layer (electronics); Feature (linguistics); Artificial intelligence; Residual; Visualization; Machine learning; Algorithm","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":[0.0003579487,0.0007603329,0.000460488,0.0005403146,0.0002691302,0.0004362309,0.001363177,0.0005456008,0.002052686],"category_scores_gemma":[0.001046072,0.0003238379,0.0002882718,0.0005348486,0.0004426166,0.001138069,0.001091472,0.0009040301,0.0007543808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006245507,"about_ca_system_score_gemma":0.0008081371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005232526,"about_ca_topic_score_gemma":0.01023296,"domain_scores_codex":[0.9997992,0.00002947941,0.000008685671,0.00006585675,0.00006733766,0.00002942407],"domain_scores_gemma":[0.9997764,0.00006058285,0.0000269785,0.00005051267,0.0000620042,0.00002356403],"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":[0.0002548126,0.00016112,0.002594403,0.0002334046,0.0001208682,0.0002187228,0.00009922683,0.2895361,0.03020407,0.03488824,0.03123231,0.6104567],"study_design_scores_gemma":[0.00002880147,0.00007374321,0.0004822167,0.00001497906,0.00001940011,0.0001215544,0.00001275915,0.9618756,0.007652141,0.01968134,0.01002376,0.00001379737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03482107,0.001460843,0.9494624,0.0003966129,0.0001824895,0.00009615808,0.0006430031,0.006445699,0.006491733],"genre_scores_gemma":[0.611543,0.001227291,0.3721226,0.0006814392,0.0001476848,0.0002397115,0.002458957,0.0004275265,0.01115179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005232526,"threshold_uncertainty_score":0.01040411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03481171050867766,"score_gpt":0.2477815070390688,"score_spread":0.2129697965303911,"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."}}