{"id":"W2924996394","doi":"10.1109/crv.2019.00017","title":"Adversarially Learned Abnormal Trajectory Classifier","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Institut de Valorisation des Données","keywords":"Discriminator; Autoencoder; Discriminative model; Artificial intelligence; Computer science; Classifier (UML); Trajectory; Binary classification; Pattern recognition (psychology); Adversarial system; Deep learning; Generative adversarial network; Event (particle physics); Machine learning; Detector; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008284433,0.0008408068,0.0008966791,0.0008586559,0.0002690525,0.0006232588,0.001424008,0.001068805,0.001107524],"category_scores_gemma":[0.003002744,0.0002557554,0.0005327767,0.0006374369,0.0006995828,0.001163158,0.0008750191,0.001966327,0.0005735475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993878,"about_ca_system_score_gemma":0.0005900945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003413809,"about_ca_topic_score_gemma":0.003560749,"domain_scores_codex":[0.9993068,0.0001154287,0.00002754629,0.0002497294,0.0001937709,0.0001066744],"domain_scores_gemma":[0.9984391,0.0005532635,0.0002565403,0.0003240315,0.0003326166,0.00009443875],"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":[0.0002524084,0.0002056661,0.01246839,0.00009130374,0.0001084773,0.0004417827,0.00008093183,0.7047862,0.01160542,0.01465909,0.009643371,0.245657],"study_design_scores_gemma":[0.000001694919,0.0000152429,0.0003842326,0.000002761606,0.000003885155,0.00005093032,0.000003267506,0.9956223,0.001601833,0.001885668,0.0004244084,0.000003765915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04984714,0.0002118473,0.9458168,0.0004071248,0.0001236807,0.00004643494,0.0004551473,0.001166251,0.001925578],"genre_scores_gemma":[0.8721494,0.0001869023,0.1201572,0.0002708121,0.000130829,0.00006044474,0.00154489,0.0001125936,0.005386937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003413809,"threshold_uncertainty_score":0.006787837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150289045242371,"score_gpt":0.2717169280692984,"score_spread":0.2402140376168747,"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."}}