{"id":"W3044932337","doi":"10.1109/ispass48437.2020.00017","title":"SeqPoint: Identifying Representative Iterations of Sequence-Based Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Advanced Micro Devices","keywords":"Computer science; Profiling (computer programming); Computation; Artificial neural network; Exploit; Convolutional neural network; Deep neural networks; Sequence (biology); Artificial intelligence; Recurrent neural network; Machine learning; Algorithm; Programming language","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":[0.001400232,0.001267171,0.0005415884,0.001254539,0.0004000249,0.0006549147,0.00105737,0.0007259256,0.001327797],"category_scores_gemma":[0.009012721,0.0005006197,0.0005630871,0.0007684347,0.0006981139,0.001474012,0.0007770661,0.001072458,0.0006552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007840538,"about_ca_system_score_gemma":0.001017144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00459628,"about_ca_topic_score_gemma":0.008724084,"domain_scores_codex":[0.9990153,0.0001511619,0.00008805186,0.0002687081,0.000374821,0.0001020354],"domain_scores_gemma":[0.9965139,0.001735201,0.0004009563,0.0004508505,0.0007805694,0.0001185709],"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":[0.001372865,0.0003309326,0.08731618,0.0009055715,0.0003331993,0.0009335247,0.0008754512,0.6042913,0.05231705,0.007425852,0.01263231,0.2312657],"study_design_scores_gemma":[0.00002775071,0.0002421274,0.01059091,0.00004702904,0.00004690049,0.0002574087,0.0001670511,0.94344,0.03572468,0.005487874,0.003924798,0.00004350545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7414549,0.001389733,0.239903,0.0002907936,0.000146646,0.0001768443,0.002934343,0.00943114,0.004272631],"genre_scores_gemma":[0.8911814,0.0003732954,0.09896614,0.0001399887,0.00002877908,0.0002321649,0.006246843,0.001214213,0.001617157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00459628,"threshold_uncertainty_score":0.009139061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111705551025448,"score_gpt":0.3580130596449512,"score_spread":0.2463075086195033,"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."}}