{"id":"W6987952452","doi":"","title":"On Using Embeddings for Ownership Verification of Graph Neural Networks","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Embedding; Graph; Artificial neural network; Benchmark (surveying); Suspect; Node (physics); Pattern recognition (psychology); Feature extraction","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":[0.0001574179,0.0002651724,0.0004318819,0.0004901327,0.0002036017,0.00002495733,0.001006251,0.0003081869,0.00000418749],"category_scores_gemma":[0.00002312567,0.0003040873,0.0003116634,0.000821965,0.00008172974,0.0003936591,0.00007909055,0.0002702929,0.000002609832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005187389,"about_ca_system_score_gemma":0.00003230825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001657385,"about_ca_topic_score_gemma":0.002382832,"domain_scores_codex":[0.9985259,0.000055986,0.0001970508,0.0005675069,0.0002994079,0.000354142],"domain_scores_gemma":[0.9982808,0.0001932454,0.0006335814,0.000558831,0.000250617,0.00008291082],"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":[0.002270601,0.0003201918,0.0004657915,0.001700417,0.0005541698,0.00007703344,0.1092925,0.7651705,0.01545119,0.04397986,0.004258369,0.05645934],"study_design_scores_gemma":[0.0009603093,0.0004809787,0.00224498,0.0004983331,0.000156291,0.000002212898,0.01454135,0.9716621,0.001938892,0.006815288,0.00003897815,0.0006603044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546364,0.00006026519,0.04318443,0.0001987124,0.001161811,0.0005393176,0.00001758311,0.0001762366,0.0000252643],"genre_scores_gemma":[0.8824953,0.0001706247,0.04460601,0.00005942049,0.0001470131,0.000003848873,0.0008543518,0.0001292487,0.07153416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2064915,"threshold_uncertainty_score":0.9999411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02383654630530054,"score_gpt":0.2419787254307739,"score_spread":0.2181421791254733,"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."}}