{"id":"W1795780783","doi":"10.1007/978-3-540-77343-6_17","title":"Selective Attention in the Learning of Viewpoint and Position Invariance","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Invariant (physics); Object (grammar); Construct (python library); Artificial neural network; Task (project management); Cognitive neuroscience of visual object recognition; Computer vision; 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":[0.001845912,0.0002259708,0.0002647557,0.0007069861,0.0001737467,0.0001731486,0.0008356362,0.0001780166,0.000003215435],"category_scores_gemma":[0.00007055851,0.0001798808,0.00006564749,0.0008438472,0.0003150389,0.0004584374,0.0002733184,0.0007448843,0.000005628347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001451073,"about_ca_system_score_gemma":0.00009868842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003635165,"about_ca_topic_score_gemma":0.0001108115,"domain_scores_codex":[0.9978316,0.0001001209,0.0004321399,0.0007138746,0.000634365,0.0002878759],"domain_scores_gemma":[0.9989032,0.0002454162,0.0002862313,0.0003544694,0.0001685799,0.00004208698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002088861,0.00008163653,0.0008210916,0.0001065784,0.00001021595,0.00004472482,0.003977537,0.01025532,0.00315114,0.1053162,0.000004194211,0.8762105],"study_design_scores_gemma":[0.0006289707,0.000949181,0.03487318,0.00104916,0.00001291379,0.0002662907,0.000004537114,0.7559607,0.002561879,0.2027198,0.0002703822,0.0007030165],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002179883,0.0001954211,0.9945199,0.0005412288,0.0003996592,0.0003025347,4.731387e-7,0.0000373019,0.001823594],"genre_scores_gemma":[0.9721281,0.0000651337,0.02680488,0.0008167383,0.00009478402,0.00000586509,0.000002276367,0.00001005017,0.00007218029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9699482,"threshold_uncertainty_score":0.7335327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333642207944792,"score_gpt":0.2811971622341837,"score_spread":0.2578607401547358,"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."}}