{"id":"W2753228334","doi":"","title":"Online Bayesian Transfer Learning for Sequential Data Modeling","year":2017,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Computer science; Artificial intelligence; Sequence (biology); Population; Context (archaeology); Machine learning; Sequence learning; Bayesian probability; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004973911,0.0001720032,0.0001605282,0.0001864398,0.001019247,0.001427575,0.004643749,0.00007436871,0.00008736163],"category_scores_gemma":[0.001503627,0.0001848241,0.00007238729,0.00004991921,0.00007956049,0.001837898,0.0008540447,0.0005423949,0.00002276453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004599972,"about_ca_system_score_gemma":0.0001386568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002961139,"about_ca_topic_score_gemma":0.00006710414,"domain_scores_codex":[0.9980847,0.0001168947,0.0003198895,0.0007911942,0.0004391806,0.0002481497],"domain_scores_gemma":[0.9976089,0.000200808,0.0001610656,0.001572315,0.0003731425,0.00008375937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000106141,0.0004729198,0.003725065,0.00002554489,0.0003057769,0.00004460653,0.002719493,0.07324625,0.004336208,0.6542604,0.001826239,0.2589314],"study_design_scores_gemma":[0.0003707061,0.00009282017,0.0004502658,0.00007528422,0.00001249299,0.000008776703,0.0001279342,0.9888248,0.0002557001,0.005906337,0.003680679,0.0001941586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009094243,0.000003830909,0.9725016,0.007441327,0.0004448367,0.0002179522,0.0001220655,0.0004025323,0.009771599],"genre_scores_gemma":[0.9229544,0.00003775014,0.07368387,0.00008707881,0.0002079117,0.00005076521,0.001251819,0.00002026159,0.001706116],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9155786,"threshold_uncertainty_score":0.9996091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2214864104494036,"score_gpt":0.4322454878549099,"score_spread":0.2107590774055063,"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."}}