{"id":"W4312763065","doi":"10.1109/icpr56361.2022.9956269","title":"Analysis of Different Deep Learning Architectures to Learn Generalised Classifier Stacking on Riemannian and Grassmann Manifolds","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Classifier (UML); Euclidean geometry; Artificial intelligence; Convolutional neural network; Pairwise comparison; Pattern recognition (psychology); Mathematics; Deep learning; Lying; Stacking; Riemannian geometry; Artificial neural network; Computer science; Pure mathematics; Geometry; Physics","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":[0.001480724,0.0007884883,0.0005074653,0.0006171718,0.0002715732,0.0006252293,0.0006476751,0.00058573,0.001662353],"category_scores_gemma":[0.004252648,0.0003075891,0.0005813382,0.000349979,0.0003975728,0.001462859,0.0006975397,0.0008486827,0.0002603095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003408,"about_ca_system_score_gemma":0.00077808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003720733,"about_ca_topic_score_gemma":0.005153119,"domain_scores_codex":[0.9995885,0.0001117312,0.00002666839,0.00007169096,0.0001249914,0.00007639765],"domain_scores_gemma":[0.9986585,0.000598055,0.0001033461,0.0001547798,0.0004030773,0.00008222985],"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":[0.000242824,0.0001201017,0.005932557,0.0001420553,0.0001619747,0.0001274424,0.00007126441,0.8384327,0.008631025,0.01966146,0.001499361,0.1249771],"study_design_scores_gemma":[0.000005091894,0.00006995212,0.0007622873,0.000009420193,0.00001607092,0.00001612957,0.00001209002,0.9928549,0.001982271,0.004025883,0.000240553,0.000005333497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5660005,0.001836446,0.4226957,0.000652325,0.00008971056,0.0001051745,0.0002514821,0.0007433292,0.007625365],"genre_scores_gemma":[0.9487087,0.0003488631,0.04839422,0.00006612967,0.00002081321,0.00006069001,0.0002897839,0.00005555309,0.002055339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003720733,"threshold_uncertainty_score":0.007830918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09741439662480494,"score_gpt":0.3205815274068795,"score_spread":0.2231671307820745,"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."}}