{"id":"W2092781340","doi":"10.1109/mlsp.2015.7324361","title":"Scalable multi-neighborhood learning for convolutional networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scalability; Scale (ratio); Artificial intelligence; Image (mathematics); Convolutional neural network; Feature (linguistics); Pattern recognition (psychology); Deep learning; Machine learning","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":[0.0001665965,0.0000923465,0.00009476778,0.00002984999,0.0001704867,0.00005698213,0.0004454371,0.00004564745,0.000007164631],"category_scores_gemma":[0.00006636242,0.00008529028,0.00004004069,0.0002943493,0.00003246933,0.0003497264,0.0001593624,0.0001182185,0.00006537123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004573977,"about_ca_system_score_gemma":0.00005403045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000319567,"about_ca_topic_score_gemma":0.000004172246,"domain_scores_codex":[0.9991093,0.00002400642,0.0001460618,0.0003051567,0.0001256215,0.0002898302],"domain_scores_gemma":[0.9991786,0.0001644401,0.00005682015,0.0002620745,0.0001754588,0.0001625945],"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":[0.000006179328,0.00006110324,0.001629338,0.000001865869,0.000008394823,6.26136e-7,0.00004312476,0.5230471,0.00006052199,0.4489797,0.01092474,0.01523733],"study_design_scores_gemma":[0.0005011941,0.00004216329,0.0003084218,0.000002307581,0.000001942186,0.000005561468,0.00001010589,0.9617665,0.00007136788,0.008311527,0.02886469,0.0001141964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009012059,0.0001489961,0.9960451,0.0008419518,0.0001879636,0.0002654203,4.390477e-7,0.0003581774,0.00206185],"genre_scores_gemma":[0.5097427,0.000006110333,0.4856953,0.0004024674,0.0001594257,0.0001440406,0.00001016229,0.00001025208,0.003829546],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5103498,"threshold_uncertainty_score":0.3478037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0506904385637424,"score_gpt":0.2926841419000356,"score_spread":0.2419937033362932,"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."}}