{"id":"W3003871186","doi":"10.1007/s10994-021-06092-6","title":"Improving sequential latent variable models with autoregressive flows","year":2021,"lang":"en","type":"article","venue":"Machine Learning","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Autoregressive model; STAR model; Latent variable; Computer science; Generalization; Benchmark (surveying); Decorrelation; Series (stratigraphy); Sequence (biology); Nonlinear autoregressive exogenous model; Autocorrelation; Latent variable model; Time series; Algorithm; Mathematics; Artificial intelligence; Machine learning; Autoregressive integrated moving average; Econometrics; Statistics","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":[0.005331558,0.001459481,0.002170089,0.001113388,0.0006395775,0.001637961,0.002335717,0.001883231,0.00420397],"category_scores_gemma":[0.01638284,0.001524445,0.001665716,0.001461859,0.0008397828,0.004659949,0.002403249,0.003767265,0.001387583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008165754,"about_ca_system_score_gemma":0.001544362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006914504,"about_ca_topic_score_gemma":0.006925276,"domain_scores_codex":[0.9982228,0.0009787607,0.00009728305,0.0003413195,0.0002313737,0.0001284915],"domain_scores_gemma":[0.9885985,0.009414631,0.0004228259,0.0008604951,0.000513189,0.0001903459],"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":[0.0002536649,0.000177993,0.001192369,0.0001121038,0.0001597483,0.0000775104,0.0001246286,0.8745018,0.0008370118,0.03708658,0.002564627,0.08291189],"study_design_scores_gemma":[0.00000688611,0.000009177536,0.00003474686,0.000003162851,0.000007024264,0.000002900674,0.000002070693,0.9915438,0.00008584139,0.008169193,0.0001326542,0.000002470487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01426869,0.0003566852,0.9839049,0.0002140773,0.00008999502,0.00001647359,0.00007897125,0.0005277973,0.0005424355],"genre_scores_gemma":[0.5701206,0.001046563,0.4179715,0.0003056734,0.0003937528,0.0002393857,0.001529577,0.0005026027,0.007890408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006914504,"threshold_uncertainty_score":0.02819633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223219842494155,"score_gpt":0.201964833962153,"score_spread":0.1897326355372115,"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."}}