{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001762776,0.0002863831,0.0002955714,0.00007095421,0.0001886552,0.0002445803,0.004023753,0.0001489349,0.00001289557],"category_scores_gemma":[0.000003713597,0.0002776609,0.00007552555,0.0005568141,0.00006567284,0.0006193959,0.004628927,0.000671329,0.00006003171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004402875,"about_ca_system_score_gemma":0.0001322619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003293592,"about_ca_topic_score_gemma":0.00005034283,"domain_scores_codex":[0.9977788,0.00007267855,0.0001563043,0.001440956,0.00008889916,0.0004624013],"domain_scores_gemma":[0.9963753,0.000116358,0.0002227306,0.003089649,0.00005940093,0.0001365447],"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":[0.000008798634,0.00001627048,0.001584718,0.00001754145,0.00004131563,0.00008019039,0.00008081805,0.9626153,0.000002737645,0.0313523,0.001363447,0.00283661],"study_design_scores_gemma":[0.0002300003,0.00002914943,0.0006290762,0.00009912635,0.00004184846,0.00001029999,0.00003194694,0.9904874,0.000001825816,0.004969115,0.003101678,0.0003685189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08471461,0.00006048961,0.910623,0.0003244041,0.000524,0.000389873,0.00001994367,0.0002761272,0.003067545],"genre_scores_gemma":[0.9918724,0.00004397511,0.006221321,0.000313876,0.0002994943,9.71898e-7,0.00006798452,0.00002197622,0.001157957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9071578,"threshold_uncertainty_score":0.9999676,"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."}}