{"id":"W3152187070","doi":"10.3390/s21144805","title":"OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection","year":2021,"lang":"en","type":"preprint","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Deep learning; Latency (audio); Computer science; Software deployment; Low latency (capital markets); Artificial intelligence; Convolutional neural network; Real-time computing; Computer network; Telecommunications; Software engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000271502,0.0004761218,0.0004981144,0.0002723183,0.0004069629,0.00043618,0.0009828581,0.0004163192,0.00001624396],"category_scores_gemma":[0.00006173937,0.0004724781,0.0004089136,0.0004968705,0.00006811885,0.00003970976,0.0004128632,0.0008009076,0.00003030549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001878185,"about_ca_system_score_gemma":0.0001340176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001426113,"about_ca_topic_score_gemma":0.0002505883,"domain_scores_codex":[0.997283,0.0001401279,0.0004973413,0.001192066,0.0003337111,0.0005537266],"domain_scores_gemma":[0.9974309,0.0003233814,0.0003867354,0.001453289,0.0002323668,0.000173281],"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":[0.00002092182,0.00007337579,0.00004215085,0.00008891684,0.00009018562,0.000008104509,0.0002635303,0.978368,0.0002948185,0.001060799,0.0005209112,0.01916822],"study_design_scores_gemma":[0.0002028103,0.0002163758,0.00193395,0.0001198754,0.0000708092,0.00003690097,0.00004201355,0.9753022,0.005169074,0.008751327,0.007486421,0.000668309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09014589,0.0001795179,0.9048903,0.0006898353,0.0007858397,0.001075896,0.00002229851,0.001160547,0.001049907],"genre_scores_gemma":[0.9095179,0.00002595613,0.08836614,0.0005445587,0.0006445647,0.0002688068,0.00002702194,0.00005666446,0.0005483875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.819372,"threshold_uncertainty_score":0.9997727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01895605087259754,"score_gpt":0.2706947675523367,"score_spread":0.2517387166797392,"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."}}