{"id":"W7132924193","doi":"","title":"Out of Distribution Detection via Normalizing Flows for Open World Machine Learning","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Set (abstract data type); Task (project management); Sample (material); Training set; Feature vector; Open set; Unsupervised 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003706959,0.001044249,0.0007875875,0.001892586,0.0008662922,0.001937228,0.002031757,0.001275072,0.001456628],"category_scores_gemma":[0.01945211,0.0005651574,0.0007032995,0.001052237,0.001939531,0.003810499,0.002692796,0.003103178,0.0008973569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001871511,"about_ca_system_score_gemma":0.001826537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005196581,"about_ca_topic_score_gemma":0.004997708,"domain_scores_codex":[0.998385,0.0004513192,0.00008043344,0.000414696,0.0005205369,0.0001480368],"domain_scores_gemma":[0.9930248,0.003531535,0.0007258132,0.001508181,0.001008213,0.0002014604],"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":[0.000262603,0.0002997523,0.01422103,0.0001289209,0.00008955279,0.0002495704,0.0004969353,0.2450565,0.01844094,0.0355668,0.008566884,0.6766205],"study_design_scores_gemma":[0.000007665673,0.00003739596,0.001036735,0.00001910618,0.000006273772,0.0000941138,0.00003938854,0.9642022,0.00994633,0.02214343,0.002447167,0.00002016176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01973877,0.0001823023,0.9757159,0.0002653353,0.00004791613,0.00006061246,0.00007571429,0.00321698,0.0006965313],"genre_scores_gemma":[0.5584319,0.0003847263,0.4359343,0.0004062703,0.0001450941,0.0002204905,0.0007676574,0.0008903877,0.002819163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005196581,"threshold_uncertainty_score":0.0196045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881087111061361,"score_gpt":0.3558758988523099,"score_spread":0.3270650277416963,"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."}}