{"id":"W2477316811","doi":"10.1109/globalsip.2016.7905908","title":"In-network linear regression with arbitrarily split data matrices","year":2016,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Regularization (linguistics); Computer science; Linear regression; Class (philosophy); Regression; Algorithm; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning; Statistics","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":[0.00307685,0.0008029688,0.0008618885,0.0003419498,0.0004640226,0.00074748,0.001469058,0.001274814,0.001502612],"category_scores_gemma":[0.009513451,0.000387644,0.0004800397,0.0006238114,0.001376183,0.002180515,0.002246806,0.001544356,0.0004392767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006573175,"about_ca_system_score_gemma":0.0008620454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274945,"about_ca_topic_score_gemma":0.002700401,"domain_scores_codex":[0.9981005,0.001033034,0.00005243687,0.000368838,0.000291298,0.0001539244],"domain_scores_gemma":[0.9953411,0.003045617,0.0004704947,0.0006961069,0.0003172219,0.000129472],"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":[0.0001424431,0.00005751611,0.0006880712,0.00004995996,0.00004728805,0.0001190568,0.0001112176,0.9431794,0.0035991,0.02441306,0.0007909915,0.0268019],"study_design_scores_gemma":[0.00000757186,0.00001739643,0.00005474507,0.000001897807,0.000002883796,0.00001727302,0.00001276853,0.9915323,0.0006937282,0.00742562,0.0002307024,0.000003011789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01510781,0.00005658699,0.9836053,0.0002052873,0.00001402803,0.00002077059,0.00002771292,0.00007655438,0.000886031],"genre_scores_gemma":[0.6627835,0.0001835936,0.3326608,0.0002343851,0.00007860635,0.0001531573,0.0002050664,0.00006261226,0.003638363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00307685,"threshold_uncertainty_score":0.01627213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612659629060339,"score_gpt":0.2533278648983135,"score_spread":0.2272012686077102,"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."}}