{"id":"W2946173218","doi":"10.1109/sampta45681.2019.9030842","title":"Reconstructing high-dimensional Hilbert-valued functions via compressed sensing","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Oak Ridge National Laboratory; Office of Science; UT-Battelle; Battelle; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Hilbert space; Reproducing kernel Hilbert space; Parameterized complexity; Mathematics; Applied mathematics; Norm (philosophy); Compressed sensing; Algorithm; 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":[0.001251101,0.0007932383,0.0006295362,0.0005661605,0.0002748317,0.0006051049,0.000948991,0.001158785,0.0008486746],"category_scores_gemma":[0.003655169,0.0002678125,0.0006599799,0.0007052634,0.001254371,0.001589233,0.001369629,0.001425459,0.0002614445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004083502,"about_ca_system_score_gemma":0.0006430452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0011435,"about_ca_topic_score_gemma":0.001114179,"domain_scores_codex":[0.9994955,0.0001647551,0.00002322309,0.0000703167,0.0002115199,0.00003467391],"domain_scores_gemma":[0.9985228,0.0009587161,0.0001565696,0.0001898508,0.0001266621,0.00004540204],"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":[0.000123501,0.00008956328,0.0007503204,0.0002351732,0.00007708483,0.0002072174,0.0001544675,0.6843742,0.03110623,0.1682691,0.001634981,0.1129782],"study_design_scores_gemma":[0.000003550764,0.00001871745,0.00004918648,0.000003654346,0.000003042359,0.00004009452,0.000005350864,0.9881892,0.002224218,0.008986021,0.0004716749,0.00000532051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003590928,0.00009162832,0.995777,0.0001065516,0.00001154291,0.000007905099,0.00001622166,0.00003989298,0.0003582809],"genre_scores_gemma":[0.3192913,0.0009794543,0.6765606,0.0001934208,0.0001445013,0.000105869,0.0002163041,0.00006065656,0.002447834],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001251101,"threshold_uncertainty_score":0.006616533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08693105610771029,"score_gpt":0.3072872394297375,"score_spread":0.2203561833220272,"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."}}