{"id":"W3103352262","doi":"","title":"DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Multigrid method; Pooling; Theoretical computer science; Laplace operator; Convolutional neural network; Graph; Kernel (algebra); Algorithm; Partial differential equation; Artificial intelligence; Mathematics; Discrete mathematics","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":[0.00005971311,0.0001773504,0.0002186522,0.00009527766,0.0001962335,0.0003741517,0.00009393996,0.00009009192,0.000007429499],"category_scores_gemma":[0.00001435085,0.0001594681,0.00005153551,0.0002467064,0.00002809224,0.001043782,0.00002299013,0.000198286,0.00001201668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002136234,"about_ca_system_score_gemma":0.00001038481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000149032,"about_ca_topic_score_gemma":7.510393e-7,"domain_scores_codex":[0.9989984,0.00001910118,0.0004620517,0.000112671,0.0001974238,0.0002103375],"domain_scores_gemma":[0.9996107,0.00001445807,0.00008305591,0.00006336905,0.00008893086,0.0001394984],"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":[0.000006049477,0.000002928315,0.0005076393,0.000354617,0.00002971547,5.794354e-7,0.000782465,0.9896924,0.000377734,0.00002777196,0.0002378971,0.007980199],"study_design_scores_gemma":[0.0002728333,0.00001178865,0.0002832606,0.00005962885,0.00002569443,0.00001057978,0.0001575445,0.9987586,0.00005218993,0.000003411743,0.0001815117,0.0001829154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3834738,0.001403109,0.6139209,0.0001131129,0.0003774057,0.0001260114,0.000009478208,0.0004898855,0.00008624917],"genre_scores_gemma":[0.9992608,0.0000292397,0.00007026068,0.0002435398,0.0002766057,0.00001633798,0.0000827629,0.0000155736,0.000004877258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.615787,"threshold_uncertainty_score":0.6502919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009552245456797056,"score_gpt":0.1863174208249627,"score_spread":0.1767651753681657,"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."}}