{"id":"W2405965731","doi":"10.1109/wacv.2016.7477730","title":"Graph matching with low-rank regularization","year":2016,"lang":"en","type":"article","venue":"","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Matching (statistics); Rank (graph theory); Mathematical optimization; Quadratic programming; Robustness (evolution); Computer science; Graph; Regularization (linguistics); Regular polygon; Quadratic equation; Blossom algorithm; Semidefinite programming; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Combinatorics","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":[0.0001441359,0.00006520164,0.0000547514,0.00006400797,0.00007986245,0.00005844095,0.0003183591,0.00002092902,0.00003049412],"category_scores_gemma":[0.000003694371,0.00003263776,0.00002317663,0.000258178,0.00003332903,0.0005315414,0.00004806193,0.00002469442,0.00004619798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004585746,"about_ca_system_score_gemma":0.00001096236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002428827,"about_ca_topic_score_gemma":0.000001819682,"domain_scores_codex":[0.99945,0.00003092276,0.00007131525,0.00018998,0.0001236524,0.0001341253],"domain_scores_gemma":[0.9995658,0.00003667313,0.00002741796,0.0002960038,0.00003092682,0.00004320565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003363348,0.00001429607,0.0001162445,0.000002376139,0.00000613031,0.000005112926,0.0001354467,0.00002052798,0.003795082,0.9653879,0.00004067592,0.03047289],"study_design_scores_gemma":[0.0004613343,0.00006920882,0.001253977,0.00006289293,0.000002719207,0.00002288756,0.0000172461,0.0007271655,0.02110944,0.9758979,0.0001913734,0.000183855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0209004,0.000005712425,0.9738001,0.000879366,0.00009067798,0.00004615387,3.259585e-7,0.0001878001,0.004089458],"genre_scores_gemma":[0.9230871,0.000003363116,0.07393686,0.0002785781,0.0000256499,0.000004283186,3.858245e-7,0.000005461377,0.002658337],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9021867,"threshold_uncertainty_score":0.1330929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004366277959415917,"score_gpt":0.1867208873695939,"score_spread":0.1823546094101779,"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."}}