{"id":"W2159993056","doi":"10.5244/c.25.132","title":"Latent Boosting for Action Recognition","year":2011,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Boosting (machine learning); Latent variable; Computer science; Machine learning; Artificial intelligence; Action recognition; Gradient boosting; Latent variable model; Margin (machine learning); Pattern recognition (psychology)","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":[0.0001080373,0.00004846339,0.00004206313,0.00005919829,0.0001031668,0.00004144505,0.00009226628,0.00003017029,0.0002195069],"category_scores_gemma":[0.00001542653,0.0000445014,0.00004004054,0.00006883607,0.000005245132,0.0005641849,0.00001855252,0.00003442226,0.0001958109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387121,"about_ca_system_score_gemma":0.000008878765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670009,"about_ca_topic_score_gemma":0.00000771078,"domain_scores_codex":[0.9995704,0.00001184354,0.0001024865,0.0001501577,0.0000560599,0.0001090229],"domain_scores_gemma":[0.9997245,0.00002332983,0.00004606545,0.00009298321,0.00008002601,0.00003306692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001138797,0.00007482293,0.0001067408,0.00001506764,0.0000121066,0.000001189421,0.0004328011,3.633933e-7,0.002524256,0.007963212,0.002046656,0.9868114],"study_design_scores_gemma":[0.001851045,0.0007474764,0.01225722,0.0000887194,0.00004516658,0.00007843323,0.0002292767,0.06361995,0.6445042,0.2564845,0.01922767,0.0008662923],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03466715,0.000003659959,0.9405742,0.00008848371,0.0003877159,0.0001502222,0.000001192661,0.0002688562,0.02385853],"genre_scores_gemma":[0.8113022,0.0000121619,0.1866371,0.0006132305,0.0001502859,0.00006846764,0.00001703469,0.000007413003,0.001192181],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9859451,"threshold_uncertainty_score":0.2516818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2685263825915866,"score_gpt":0.2921282165624963,"score_spread":0.02360183397090976,"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."}}