{"id":"W2614475194","doi":"10.15353/vsnl.v1i1.42","title":"Stochastic Receptive Fields in Deep Convolutional Networks","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Receptive field; Convolutional neural network; Computer science; Conditional random field; Artificial intelligence; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001395716,0.00009426365,0.00009639896,0.00008272345,0.00005201934,0.00003479779,0.0004380417,0.00004895566,0.00000780735],"category_scores_gemma":[0.00003583206,0.00009158517,0.00002720935,0.0004938992,0.00005068006,0.0003164952,0.0001666696,0.000197185,0.00008128789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000869609,"about_ca_system_score_gemma":0.0000189246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007255268,"about_ca_topic_score_gemma":0.00001677438,"domain_scores_codex":[0.9990451,0.00005141498,0.0001684137,0.0003053359,0.0001954782,0.0002342652],"domain_scores_gemma":[0.9993149,0.0001615924,0.00005653115,0.0003168676,0.00004053029,0.0001095501],"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":[0.00001229309,0.00003478387,0.0001599934,7.857449e-7,0.00000306984,0.00000968828,0.0002656217,0.9540995,0.0002711057,0.01241824,0.01686909,0.01585584],"study_design_scores_gemma":[0.0003595461,0.00004355868,0.002184212,0.00001339885,0.000001128916,0.000008293674,0.00001275916,0.9920435,0.00001112135,0.003697818,0.001485345,0.0001393242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009312839,0.00007519206,0.9785792,0.01134928,0.0003145637,0.0001514283,3.763546e-7,0.00008861002,0.0001285405],"genre_scores_gemma":[0.9618927,0.000003605455,0.03223839,0.00564945,0.0001319561,0.00003956795,0.000004847268,0.000006889402,0.00003257297],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9525799,"threshold_uncertainty_score":0.3734735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949999173153214,"score_gpt":0.2724502745247842,"score_spread":0.252950282793252,"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."}}