{"id":"W2950887295","doi":"","title":"GMNN: Graph Markov Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Statistical relational learning; Computer science; Artificial intelligence; Conditional random field; Graph; Markov random field; Artificial neural network; Convolutional neural network; Pattern recognition (psychology); Object (grammar); Theoretical computer science; Machine learning; Relational database; Data mining","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":[0.0006529454,0.0007982331,0.0007616933,0.0008514374,0.0004036412,0.0007788275,0.001751619,0.001306675,0.003398535],"category_scores_gemma":[0.004126672,0.0004695833,0.000638683,0.00124755,0.0008805165,0.001730061,0.001104044,0.00170976,0.001115519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145442,"about_ca_system_score_gemma":0.0009328444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084119,"about_ca_topic_score_gemma":0.01281633,"domain_scores_codex":[0.9996074,0.0001160477,0.00001421516,0.0001346981,0.00008965396,0.00003809824],"domain_scores_gemma":[0.9989005,0.0006454819,0.0001243211,0.0001336628,0.0001493046,0.00004661245],"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":[0.000107539,0.00005781653,0.001138872,0.000184622,0.00008389567,0.0001047617,0.00006171848,0.7603655,0.001602803,0.06665017,0.01257904,0.1570633],"study_design_scores_gemma":[0.000003687091,0.000006328757,0.00009810489,0.000007494063,0.000003897257,0.00001408656,0.000002790168,0.9701163,0.0001907226,0.02839866,0.00115407,0.000003960956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01570278,0.00210014,0.972012,0.00112657,0.00023016,0.00006438272,0.001077587,0.003021387,0.004664823],"genre_scores_gemma":[0.6223208,0.002843429,0.3562299,0.001089955,0.0003089147,0.0003607169,0.004447377,0.0005452647,0.01185378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01084119,"threshold_uncertainty_score":0.0215562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0398459198662946,"score_gpt":0.1781134981141955,"score_spread":0.1382675782479009,"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."}}