{"id":"W2957236078","doi":"10.48550/arxiv.1907.05550","title":"Faster Neural Network Training with Data Echoing","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Training set; Machine learning; Psychology; Geography","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":[0.001537588,0.00103609,0.0007970595,0.0006021762,0.0004250481,0.001074714,0.001958878,0.0009313408,0.004474718],"category_scores_gemma":[0.01016803,0.0007084929,0.000458034,0.001095901,0.0007372101,0.004188922,0.001262237,0.002208006,0.001300815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008986091,"about_ca_system_score_gemma":0.001400427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007338827,"about_ca_topic_score_gemma":0.009123895,"domain_scores_codex":[0.9989334,0.0001380813,0.00009496102,0.0003552629,0.0003346953,0.0001436201],"domain_scores_gemma":[0.9956176,0.002023122,0.0001893829,0.001288939,0.0007218731,0.0001590954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002644049,0.0009595464,0.0109499,0.0005526632,0.0002992047,0.0003016748,0.0005206228,0.2782827,0.1007176,0.008041961,0.02490461,0.5718254],"study_design_scores_gemma":[0.0001035665,0.0002415727,0.001584994,0.00001753918,0.00003168146,0.00006542658,0.00006320109,0.9422471,0.04896379,0.002873529,0.003773888,0.00003373561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5358704,0.001600562,0.3978131,0.001451898,0.000900182,0.0002132104,0.001012218,0.05031875,0.0108197],"genre_scores_gemma":[0.7723919,0.0002482539,0.2190538,0.0004809935,0.0001048633,0.0001673508,0.001634515,0.001114813,0.004803644],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007338827,"threshold_uncertainty_score":0.01496947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1686051216013259,"score_gpt":0.204130531865589,"score_spread":0.03552541026426304,"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."}}