{"id":"W4401211718","doi":"10.1109/isca59077.2024.00081","title":"Heterogeneous Acceleration Pipeline for Recommendation System Training","year":2024,"lang":"en","type":"article","venue":"","topic":"Education and Learning Interventions","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Acceleration; Computer science; Pipeline (software); Training (meteorology); Recommender system; Machine learning; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003023487,0.00004957295,0.00004832679,0.00009086977,0.00009930084,0.0003916835,0.0001345925,0.00002354981,0.0001384614],"category_scores_gemma":[0.00002342881,0.00004482573,0.00006822891,0.0001417609,0.000003472901,0.0002877618,0.00001645779,0.00004638955,0.00009878277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005059746,"about_ca_system_score_gemma":0.00004299343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000587657,"about_ca_topic_score_gemma":0.000006904779,"domain_scores_codex":[0.9994775,0.0000346475,0.0001635169,0.0001849344,0.00004745596,0.00009192461],"domain_scores_gemma":[0.9997036,0.00007987148,0.00002315992,0.0001082137,0.00005398907,0.00003112142],"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":[0.000001485888,0.00003060796,0.00001108116,0.0001326788,0.00001984004,9.391696e-7,0.002594471,0.0005398986,0.0002441304,0.4280615,0.02787701,0.5404863],"study_design_scores_gemma":[0.0000553647,0.00002761841,0.00001729109,0.00004587073,0.000003554785,0.00002135653,0.0002628717,0.8365411,0.0003489938,0.0002435455,0.1623763,0.00005611268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006308551,0.00004137129,0.9868044,0.00592158,0.001514824,0.0001313955,0.000001685751,0.0006036262,0.004350314],"genre_scores_gemma":[0.9581151,0.000001223689,0.03588161,0.0002275154,0.0001800285,0.00006955736,0.00003828983,0.000006509672,0.005480152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9574842,"threshold_uncertainty_score":0.3777013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09708145438284371,"score_gpt":0.3521146602644055,"score_spread":0.2550332058815618,"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."}}