{"id":"W2162377461","doi":"10.1109/have.2003.1244721","title":"Neural network architecture for 3D object representation","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Morphing; Object (grammar); Representation (politics); Artificial intelligence; Artificial neural network; Set (abstract data type); Feedforward neural network; Computer vision; Architecture; Cognitive neuroscience of visual object recognition; Object model; Transformation (genetics)","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":[0.0003004442,0.0003671355,0.000332583,0.0003682816,0.0002116979,0.000649645,0.0008388641,0.0009829379,0.003229335],"category_scores_gemma":[0.000703782,0.0002285054,0.00040464,0.0007455436,0.000335395,0.0008359891,0.0005303905,0.0006764586,0.001272405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006310738,"about_ca_system_score_gemma":0.0004607963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005225884,"about_ca_topic_score_gemma":0.005106166,"domain_scores_codex":[0.9998606,0.00002706469,0.000008626617,0.00002915065,0.00006116304,0.00001334704],"domain_scores_gemma":[0.9998984,0.00002983572,0.000008576134,0.00001681144,0.00004109934,0.000005195203],"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":[0.00008235308,0.00002614021,0.0004048025,0.0001479357,0.00005787547,0.0001034633,0.00005257644,0.6478236,0.01794963,0.04094636,0.004103262,0.288302],"study_design_scores_gemma":[0.000003883441,0.00001653531,0.0001366676,0.00001216255,0.000009531937,0.00003199562,0.000003878001,0.9842985,0.001984456,0.009537235,0.003957591,0.000007547909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004213034,0.0008405052,0.9903814,0.0001903136,0.00006343435,0.00001647586,0.00008794095,0.0009276638,0.003279137],"genre_scores_gemma":[0.3641077,0.003156709,0.617422,0.0003098484,0.0001179499,0.000260928,0.0007798594,0.0001288455,0.01371611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005225884,"threshold_uncertainty_score":0.01080322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399667446774282,"score_gpt":0.2606697797174756,"score_spread":0.2466731052497327,"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."}}