{"id":"W2971130703","doi":"10.1109/tpami.2019.2937294","title":"Deep Differentiable Random Forests for Age Estimation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face recognition and analysis","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University Health Network","funders":"National Natural Science Foundation of China","keywords":"Deep learning; Differentiable function; Random forest; Regression; Pattern recognition (psychology); Feature (linguistics); Convolutional neural network; Tree (set theory)","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":[0.001846785,0.001069738,0.001227065,0.0009825297,0.0002748973,0.0004851861,0.001543242,0.001022955,0.001953596],"category_scores_gemma":[0.003912117,0.0004925402,0.001083758,0.0008998776,0.0003755537,0.001132867,0.0005963171,0.001428108,0.00131441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006509778,"about_ca_system_score_gemma":0.0007342562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00719107,"about_ca_topic_score_gemma":0.01168497,"domain_scores_codex":[0.9993117,0.0002099576,0.00003063294,0.0002303994,0.0001203919,0.00009693172],"domain_scores_gemma":[0.9988618,0.0005868578,0.0001393906,0.0001403356,0.0002249126,0.00004667705],"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":[0.0002167016,0.0001274174,0.005491215,0.0001255648,0.0001210547,0.0001425089,0.00006909724,0.5963382,0.005123811,0.009364366,0.007326349,0.3755537],"study_design_scores_gemma":[0.000005234227,0.0000100426,0.0004923083,0.000008152843,0.000007930755,0.00003237737,0.000004846827,0.9911438,0.000786663,0.006828258,0.0006739359,0.000006517251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01072534,0.001058834,0.9858837,0.0001454054,0.00004841213,0.00002177053,0.000362423,0.001259733,0.0004944988],"genre_scores_gemma":[0.5526807,0.001340193,0.4374073,0.0002994361,0.0002396958,0.0001546102,0.002813729,0.0003240416,0.004740257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00719107,"threshold_uncertainty_score":0.01429844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582440736541821,"score_gpt":0.2618737843582439,"score_spread":0.2460493769928257,"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."}}