{"id":"W3070904253","doi":"10.48550/arxiv.2008.08838","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Training (meteorology); Perspective (graphical); Expressive power; Convolutional neural network; Artificial intelligence; Machine learning; Operator (biology); Limit (mathematics); Risk analysis (engineering); Theoretical computer science; Mathematics; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001820713,0.0004613212,0.0006336432,0.0003046538,0.0001806489,0.00008683415,0.002267491,0.0003397031,0.000023072],"category_scores_gemma":[0.00002425484,0.000555644,0.0005088838,0.001114965,0.0002851195,0.0005602152,0.002307052,0.001039477,0.000007087097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008363751,"about_ca_system_score_gemma":0.00009545337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003713187,"about_ca_topic_score_gemma":0.00001058888,"domain_scores_codex":[0.9971814,0.0002248264,0.0004370668,0.001385681,0.0001746918,0.0005963796],"domain_scores_gemma":[0.9977815,0.0002192513,0.000610601,0.0009413617,0.000185375,0.0002619324],"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":[0.00003096536,0.0000255062,0.0007937977,0.00003976556,0.0001168791,0.0002589738,0.0001759661,0.9124576,0.00006807728,0.08478626,0.000199414,0.001046788],"study_design_scores_gemma":[0.0003855212,0.00004727212,0.0009161045,0.0001161502,0.00007269846,0.00001478219,0.00006385609,0.9069045,0.0000369706,0.09089883,0.0000579647,0.0004853843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04012547,0.0002049505,0.957251,0.0005077726,0.00116352,0.000290888,0.00001606901,0.0003020423,0.0001383203],"genre_scores_gemma":[0.9920242,0.00009680479,0.006695696,0.0008862554,0.0001892348,0.000001216992,0.00003016899,0.00003229643,0.00004406269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9518988,"threshold_uncertainty_score":0.9996895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043788125263583,"score_gpt":0.2004482211609255,"score_spread":0.09606940863456725,"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."}}