{"id":"W3098572035","doi":"10.48550/arxiv.1110.0895","title":"Robust inversion via semistochastic dimensionality reduction","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dimensionality reduction; Curse of dimensionality; Computer science; Inversion (geology); Missing data; Inverse problem; Algorithm; Synthetic data; Mathematical optimization; Robust statistics; Inverse; Data mining; Mathematics; Artificial intelligence; Machine learning","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":[0.00264839,0.0008478278,0.001045711,0.0008377706,0.000398022,0.001068605,0.001074444,0.001082626,0.001202983],"category_scores_gemma":[0.006685646,0.0005645665,0.001026282,0.0009223516,0.002178329,0.001584003,0.002109875,0.001392308,0.0005191675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005127107,"about_ca_system_score_gemma":0.00111689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008357693,"about_ca_topic_score_gemma":0.0007352785,"domain_scores_codex":[0.9982357,0.0006391093,0.00007944159,0.0003065059,0.0006595911,0.00007955409],"domain_scores_gemma":[0.9974126,0.001258243,0.0004189623,0.0005870169,0.0002653301,0.00005771457],"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":[0.0001837697,0.0000812646,0.0009305159,0.0002540275,0.0001539369,0.00015165,0.0001873242,0.7061806,0.03338315,0.1475418,0.001625276,0.1093267],"study_design_scores_gemma":[0.00001101837,0.00005557837,0.0001992001,0.00001067607,0.000007360772,0.00006278484,0.0000158534,0.9438139,0.005507598,0.04921057,0.001085164,0.00002041627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005880795,0.00008419742,0.9930974,0.0001348757,0.00001371652,0.00001812781,0.00005215691,0.0001108728,0.000607872],"genre_scores_gemma":[0.2932421,0.0006153719,0.7020491,0.0002640291,0.0001201335,0.000344717,0.0004417609,0.0001298972,0.002792872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00264839,"threshold_uncertainty_score":0.01400614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048227659795836,"score_gpt":0.1677297611080322,"score_spread":0.06290699512844866,"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."}}