{"id":"W2980241036","doi":"10.1109/lsp.2019.2945683","title":"Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Encoder; Thresholding; Joint (building); Artificial intelligence; Sparse matrix; Computational complexity theory; Matrix (chemical analysis); Compressed sensing; Algorithm; Computation; Signal processing; Pattern recognition (psychology); Computer engineering; Computer hardware; Digital signal processing; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006090328,0.0002450641,0.0003562817,0.0001709542,0.00004720288,0.00006212696,0.0001349161,0.00009472737,0.00004226814],"category_scores_gemma":[0.000007868156,0.0002401836,0.00006903003,0.0001410223,0.00004628802,0.0002354082,0.00002360324,0.000207557,0.00001576842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008386043,"about_ca_system_score_gemma":0.00003910912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001481916,"about_ca_topic_score_gemma":6.681515e-7,"domain_scores_codex":[0.9985618,0.0000794567,0.0003635419,0.0002855733,0.0004019957,0.0003076115],"domain_scores_gemma":[0.9994519,0.00004674408,0.0001191877,0.0002073083,0.0001065211,0.00006833916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002490876,0.00001300263,0.00003134424,0.0001582758,0.00004967572,0.00001247552,0.0001697654,0.131892,0.8375432,0.000001825204,0.001634618,0.02846885],"study_design_scores_gemma":[0.0002858815,0.0001097051,0.0001427062,0.0003433607,0.00006653488,0.00004982537,0.00002434708,0.4867198,0.5112321,0.0004458801,0.0002113399,0.0003684845],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03534437,0.0004701585,0.9630665,0.0001114544,0.0001871853,0.0002694941,0.000001251291,0.0003654562,0.0001841597],"genre_scores_gemma":[0.882069,0.00001473568,0.1174555,0.0003131985,0.00006377749,0.00001037234,0.000001196616,0.00005440415,0.00001788368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8467246,"threshold_uncertainty_score":0.9794403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03331377140293759,"score_gpt":0.2434455680669855,"score_spread":0.2101317966640479,"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."}}