{"id":"W4312709677","doi":"10.1109/cvpr52688.2022.01005","title":"Exploiting Explainable Metrics for Augmented SGD","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Computer science; Stochastic gradient descent; Generalization; Exploit; Artificial intelligence; Machine learning; Overhead (engineering); Deep learning; Learning to rank; Artificial neural network; Measure (data warehouse); Rank (graph theory); Layer (electronics); Deep neural networks; Network architecture; Data mining; Ranking (information retrieval); Mathematics","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":[0.003246328,0.001651138,0.001493749,0.001182736,0.0004821973,0.001356171,0.00165443,0.00168898,0.001589023],"category_scores_gemma":[0.009984202,0.0005666034,0.0009427547,0.0008028345,0.001606877,0.002245382,0.002825754,0.002214967,0.0004279409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269375,"about_ca_system_score_gemma":0.00142427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003085192,"about_ca_topic_score_gemma":0.004171084,"domain_scores_codex":[0.9988139,0.0005307759,0.00008614657,0.0002083848,0.0002871153,0.0000736909],"domain_scores_gemma":[0.9955915,0.002323905,0.0004334879,0.0009226424,0.0005441161,0.000184316],"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":[0.000105921,0.00006337544,0.002582147,0.0001161434,0.00008491707,0.0001100089,0.0001120016,0.8695936,0.004675176,0.04435409,0.002112521,0.07609005],"study_design_scores_gemma":[0.000005446684,0.00002709845,0.0001099191,0.000004245062,0.000002949451,0.00001052264,0.000004820582,0.9881489,0.0004614212,0.01093554,0.0002840726,0.000005207969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01686376,0.0002240021,0.9813246,0.0002315011,0.00002748534,0.0000340833,0.00009163434,0.0006316857,0.0005712424],"genre_scores_gemma":[0.5447745,0.0003324767,0.4505683,0.000360649,0.0001005547,0.0002914881,0.0009950647,0.0005785534,0.001998483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003246328,"threshold_uncertainty_score":0.0171684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07788462505945132,"score_gpt":0.3020840148417718,"score_spread":0.2241993897823205,"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."}}