{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003416718,0.0001173788,0.0001049501,0.00001666761,0.0001268616,0.0000492654,0.0006627007,0.00003750506,0.00005472816],"category_scores_gemma":[0.00001882777,0.0001089179,0.00005097272,0.0004825991,0.00005361347,0.000355419,0.0002753032,0.0001629288,0.0001309747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001865346,"about_ca_system_score_gemma":0.00002196257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002599951,"about_ca_topic_score_gemma":0.000002671603,"domain_scores_codex":[0.9989774,0.00003163673,0.0001654334,0.0003949283,0.0001663389,0.0002642619],"domain_scores_gemma":[0.9992933,0.0001129981,0.00005533253,0.0002620793,0.00006144781,0.0002148274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001851243,0.00003328507,0.0004562621,0.000002267885,0.00001465178,0.00001919603,0.0001261367,0.2662121,0.0004652514,0.6763333,0.02873869,0.02758035],"study_design_scores_gemma":[0.0001501709,0.00004513468,0.001561007,9.938725e-7,0.000001953704,0.00001305102,0.00000499713,0.9855196,0.00007176874,0.001642163,0.01085544,0.0001336438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001086325,0.0001449102,0.9840154,0.01239267,0.00009842884,0.0001555068,0.000001539113,0.0004331653,0.001672071],"genre_scores_gemma":[0.9143636,0.00001005535,0.07550556,0.009618398,0.0002625911,0.00002692936,0.000004102495,0.0000088522,0.0001998671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9132773,"threshold_uncertainty_score":0.4441541,"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."}}