{"id":"W2945192089","doi":"10.1609/aaai.v34i04.6039","title":"Bivariate Beta-LSTM","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; Korea Advanced Institute of Science and Technology; Agency for Defense Development","keywords":"Sigmoid function; Bivariate analysis; Beta distribution; Computer science; Function (biology); Prior probability; Skewness; Algorithm; Artificial intelligence; Image (mathematics); Probabilistic logic; Term (time); Mathematics; Pattern recognition (psychology); Statistics; Machine learning; Bayesian probability; Physics; Artificial neural network","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":[0.0003885638,0.0007777732,0.0005496298,0.0004167437,0.0001981617,0.000652972,0.001010944,0.0006299348,0.004736744],"category_scores_gemma":[0.001856177,0.0003325361,0.0004766711,0.0006896352,0.0004074475,0.001607765,0.0007845213,0.001209534,0.001568991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006313566,"about_ca_system_score_gemma":0.0006800584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002427361,"about_ca_topic_score_gemma":0.003446689,"domain_scores_codex":[0.9997864,0.00004144455,0.00001039551,0.00009110834,0.00003894451,0.00003168196],"domain_scores_gemma":[0.9995987,0.0001502136,0.00003850148,0.00006959208,0.0001111023,0.00003193912],"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":[0.0004302814,0.0001101197,0.002732317,0.0002401597,0.0001252613,0.0002756615,0.0001708987,0.5150195,0.03074205,0.05444073,0.01366453,0.3820485],"study_design_scores_gemma":[0.000006351764,0.0000253299,0.0004377575,0.00001007694,0.00001458306,0.00006347219,0.00000788648,0.9792796,0.003378612,0.01528505,0.001483274,0.000008016596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03318777,0.0007237861,0.955186,0.0004894886,0.0001476973,0.00002638217,0.0008579751,0.002507477,0.006873374],"genre_scores_gemma":[0.8662447,0.0008622525,0.1219401,0.0003868813,0.0001286236,0.00009236632,0.001429299,0.0003528939,0.008562917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004736744,"threshold_uncertainty_score":0.01584601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1583393092705185,"score_gpt":0.316150595604048,"score_spread":0.1578112863335295,"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."}}