{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00226517,0.001476441,0.002299667,0.001942519,0.0008944984,0.002068766,0.00236299,0.002705702,0.003884989],"category_scores_gemma":[0.009351343,0.0008108087,0.001526549,0.003007508,0.001399596,0.003249903,0.001964025,0.002795598,0.001654762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224212,"about_ca_system_score_gemma":0.001968426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005183382,"about_ca_topic_score_gemma":0.00561188,"domain_scores_codex":[0.9974612,0.0008668737,0.0001094447,0.0007070478,0.0006845273,0.0001709205],"domain_scores_gemma":[0.9967002,0.001637303,0.0003880447,0.0006796953,0.0004586591,0.0001360339],"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":[0.0001415736,0.0001572253,0.0007880201,0.0002858256,0.0001269681,0.0002275059,0.0001038985,0.7509796,0.005321574,0.055661,0.01371442,0.1724924],"study_design_scores_gemma":[0.000008947525,0.00001295957,0.00006801035,0.000005084924,0.000006043284,0.00004057689,0.00001013033,0.9807747,0.0006765324,0.01734668,0.001042821,0.000007569422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003345488,0.0001476421,0.9948061,0.0001705035,0.00002788655,0.00003185883,0.00007776819,0.000389078,0.001003647],"genre_scores_gemma":[0.2328364,0.0005113242,0.7560498,0.000535396,0.0002000252,0.0002595529,0.00135686,0.0005974979,0.007653055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005183382,"threshold_uncertainty_score":0.01299661,"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."}}