{"id":"W4287816729","doi":"10.5281/zenodo.4323247","title":"Improving Performance of Low-Rank Matrix Completion Algorithms Through Debiasing of Sampling Patterns","year":2020,"lang":"en","type":"dissertation","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Debiasing; Matrix completion; Rank (graph theory); Computer science; Sampling (signal processing); Matrix (chemical analysis); Low-rank approximation; Algorithm; Mathematics; Psychology; Combinatorics; Computer vision; Materials science; Social psychology; Pure mathematics","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":[0.003701634,0.001732677,0.001497667,0.0008230225,0.0006719771,0.001518867,0.001787746,0.001610859,0.004097214],"category_scores_gemma":[0.02343591,0.0005433438,0.0007266769,0.001077418,0.001681307,0.003372343,0.002247995,0.002817156,0.002395737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006920231,"about_ca_system_score_gemma":0.001579168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010106,"about_ca_topic_score_gemma":0.003566328,"domain_scores_codex":[0.9975495,0.0009325005,0.0001501816,0.0003914718,0.0007553376,0.0002210352],"domain_scores_gemma":[0.989152,0.005576399,0.0006055401,0.002789625,0.001457318,0.0004192378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001479844,0.0005380003,0.002234411,0.0003893422,0.0001879336,0.000190042,0.0005691679,0.5101257,0.0336641,0.04973925,0.0110824,0.3897998],"study_design_scores_gemma":[0.0000494165,0.0001241411,0.0001714933,0.00001153275,0.000007234109,0.00005910766,0.00003747411,0.9792797,0.007555099,0.0117168,0.0009693226,0.00001873667],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03550144,0.0004182343,0.9601024,0.0003095278,0.00008039554,0.00006017376,0.0000792514,0.001702039,0.001746578],"genre_scores_gemma":[0.2426126,0.0003880991,0.7517026,0.0002403565,0.00009967352,0.0001148466,0.0004747776,0.0003912256,0.003975878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004097214,"threshold_uncertainty_score":0.01957631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03604506630042849,"score_gpt":0.2763011127918099,"score_spread":0.2402560464913814,"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."}}