{"id":"W2600555690","doi":"10.15353/vsnl.v2i1.106","title":"StochasticNet in StochasticNet","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Deep neural networks; Deep learning; Artificial neural network; Artificial intelligence; Convolutional neural network; Graph; Machine learning; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007566671,0.0009361195,0.0006455025,0.000538531,0.0003855939,0.00146637,0.001162077,0.001020929,0.01473636],"category_scores_gemma":[0.001832174,0.0004769876,0.0008295379,0.0005827498,0.00103012,0.0017834,0.001528286,0.002084541,0.004408876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009542229,"about_ca_system_score_gemma":0.001372151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004405842,"about_ca_topic_score_gemma":0.008370814,"domain_scores_codex":[0.9993431,0.0001458407,0.0000436439,0.0001816846,0.0002214947,0.00006431557],"domain_scores_gemma":[0.9996111,0.0001066732,0.00004811216,0.00009831199,0.00009151616,0.00004425994],"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":[0.0002277558,0.0001256691,0.002060925,0.000526833,0.0001545113,0.0003359855,0.0001084917,0.2953736,0.01203171,0.4211447,0.05852268,0.2093872],"study_design_scores_gemma":[0.00003482847,0.00008312284,0.000442709,0.00006971737,0.00002970817,0.0002056739,0.00001877513,0.7493978,0.007170959,0.1421008,0.1004107,0.00003518263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009705077,0.001828784,0.9502817,0.001809772,0.001251977,0.000125241,0.001721455,0.00706355,0.02621248],"genre_scores_gemma":[0.342731,0.003788267,0.5895241,0.002874996,0.001148774,0.0007686038,0.008573717,0.002752368,0.0478382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01473636,"threshold_uncertainty_score":0.04929799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009665562301139371,"score_gpt":0.2787130577608325,"score_spread":0.2690474954596931,"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."}}