{"id":"W3110866490","doi":"10.1109/tsipn.2020.3044955","title":"Anytime Minibatch With Delayed Gradients","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Science and Engineering Research Council; Huawei Technologies","keywords":"Asynchronous communication; Regret; Convergence (economics); Transmission (telecommunications); Range (aeronautics); Variable (mathematics); Convex optimization; Optimization problem","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":[],"consensus_categories":[],"category_scores_codex":[0.0000735498,0.0001623615,0.0001348096,0.0001141,0.0002667351,0.000316248,0.0002197712,0.00007442355,0.00002782636],"category_scores_gemma":[0.000001811956,0.000135613,0.00002966156,0.0006253967,0.00005314413,0.00367595,0.000003385008,0.0001969139,0.000009625692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002288022,"about_ca_system_score_gemma":0.00004677136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003950822,"about_ca_topic_score_gemma":4.114635e-7,"domain_scores_codex":[0.9990473,0.00001649198,0.0002810287,0.0001905043,0.000268032,0.0001966716],"domain_scores_gemma":[0.9994256,0.00003228126,0.0001419261,0.0001138923,0.0001268051,0.0001594641],"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":[0.0002615632,0.00008572439,0.00003155925,0.00006647679,0.00005675028,0.000002813037,0.004196286,0.6286501,0.00002520019,0.001712841,0.00113075,0.3637799],"study_design_scores_gemma":[0.0004896573,0.0003936948,0.00004697393,0.0000510724,0.00001508117,0.0000131572,0.00003480942,0.9973181,0.0008263623,0.00008754519,0.0005331147,0.0001903791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006980413,0.00002546785,0.9976397,0.0004314031,0.00007358665,0.0001918331,0.000004493246,0.0003830658,0.0005523654],"genre_scores_gemma":[0.9597712,0.00002112365,0.03718096,0.002934964,0.00002326722,0.00003395755,0.000005621237,0.000008156936,0.00002072155],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9604588,"threshold_uncertainty_score":0.5530139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009260081566282828,"score_gpt":0.201593267834265,"score_spread":0.1923331862679822,"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."}}