{"id":"W3186432075","doi":"10.1016/j.patcog.2021.108202","title":"Graph variational auto-encoder for deriving EEG-based graph embedding","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adjacency matrix; Computer science; Autoencoder; Graph; Embedding; Graph embedding; Adjacency list; Pattern recognition (psychology); Encoder; Artificial intelligence; Electroencephalography; Algorithm; Theoretical computer science; Deep learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002571767,0.0001677408,0.0001618951,0.0001589065,0.0004446122,0.00009569096,0.00008481853,0.00006997833,0.0002714177],"category_scores_gemma":[0.004027165,0.0001873948,0.000161971,0.0003495384,0.0000499513,0.0002731154,0.00004537297,0.0001334223,0.00008057576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005913656,"about_ca_system_score_gemma":0.0000683754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008442827,"about_ca_topic_score_gemma":0.00003370106,"domain_scores_codex":[0.9983813,0.0001592012,0.0002253266,0.0006138119,0.0003343615,0.0002860053],"domain_scores_gemma":[0.9942326,0.005209306,0.0001133622,0.0001456093,0.0002457505,0.0000533795],"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":[0.0001969735,0.001021109,0.0181021,0.0004991021,0.0001605361,0.0001043209,0.000798264,0.006553263,0.7578927,0.001115074,0.01444188,0.1991147],"study_design_scores_gemma":[0.003724804,0.0002685383,0.04395565,0.000413622,0.0001266349,0.0001113136,0.0002537761,0.1258673,0.7287087,0.08947874,0.005792104,0.001298825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1527715,0.00004230065,0.8404887,0.004368495,0.0009871525,0.0003248804,0.0002737578,0.0001875529,0.0005556241],"genre_scores_gemma":[0.9849952,0.00001534037,0.004246244,0.009979939,0.000217212,0.0003063628,0.000148394,0.00003223448,0.00005900844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8362424,"threshold_uncertainty_score":0.7641739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06506920422645793,"score_gpt":0.2903136568654437,"score_spread":0.2252444526389858,"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."}}