{"id":"W2440384215","doi":"10.1109/cvpr.2016.614","title":"Efficient Deep Learning for Stereo Matching","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":803,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Exploit; Computer science; Concatenation (mathematics); Computation; Matching (statistics); Convolutional neural network; Artificial intelligence; Product (mathematics); Layer (electronics); Class (philosophy); Artificial neural network; Pattern recognition (psychology); Algorithm; Mathematics; Arithmetic","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":[0.000132382,0.00005725868,0.00005914564,0.00004078322,0.0001120856,0.00005667378,0.0002616314,0.00001076327,0.00002735209],"category_scores_gemma":[0.00003261712,0.00003325861,0.0000351142,0.00006667485,0.000009855034,0.0001478595,0.0001338554,0.00003189301,0.00008584562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001792982,"about_ca_system_score_gemma":0.00000643925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.741417e-7,"about_ca_topic_score_gemma":4.991911e-7,"domain_scores_codex":[0.9994126,0.00001364253,0.00009371553,0.0002005638,0.00009173054,0.0001877651],"domain_scores_gemma":[0.9995727,0.0001471165,0.00003150047,0.000166992,0.00003321756,0.00004845552],"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":[0.000001793912,0.00001181132,0.00007243004,0.000002940968,0.000001697985,7.781115e-7,0.0001888479,0.001222694,0.007301782,0.04830655,0.00004634526,0.9428423],"study_design_scores_gemma":[0.0005892625,0.00005052078,0.0003738417,0.00004122905,0.000001074229,0.000006641572,0.0001061753,0.9669304,0.004877539,0.007416793,0.01943531,0.0001711968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005625912,0.0000230203,0.9906498,0.0009171381,0.0001602835,0.0000682201,7.149764e-8,0.000195004,0.002360509],"genre_scores_gemma":[0.6470422,0.000001673958,0.350559,0.0003068045,0.0000226134,0.000005297817,6.529776e-8,0.000004831299,0.002057579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9657077,"threshold_uncertainty_score":0.1356247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328755608136784,"score_gpt":0.2795611911963601,"score_spread":0.2662736351149922,"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."}}