{"id":"W3128955336","doi":"10.48550/arxiv.2102.05208","title":"Attentive Gaussian processes for probabilistic time-series generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Kernel (algebra); Sequence (biology); Probabilistic logic; Computation; Range (aeronautics); Scalability; Artificial neural network; Artificial intelligence; Gaussian process; Machine learning; Block (permutation group theory); Representation (politics); Process (computing); Algorithm; Gaussian; Mathematics","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":[0.000144747,0.0003690126,0.0003851237,0.0001499658,0.0002790966,0.0006321957,0.001376572,0.0002669368,0.00003764985],"category_scores_gemma":[0.0001679545,0.0003974623,0.0001676398,0.0006956051,0.0001194655,0.0009772772,0.001012455,0.0002751739,0.00003179003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001417504,"about_ca_system_score_gemma":0.001107837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001880589,"about_ca_topic_score_gemma":0.00009138912,"domain_scores_codex":[0.9977776,0.00007168002,0.0002457091,0.001422137,0.00009118388,0.0003916284],"domain_scores_gemma":[0.99782,0.00007636443,0.0003078003,0.0008865962,0.0007646683,0.000144625],"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":[0.0001343612,0.0008533599,0.00226937,0.006963278,0.0005682565,0.0005792828,0.002936556,0.1423159,0.001123423,0.834267,0.003149852,0.004839319],"study_design_scores_gemma":[0.0008407679,0.0003308788,0.001066311,0.0006748466,0.0002653197,0.00003387894,0.0002104646,0.8151833,0.002817979,0.175662,0.00116537,0.00174889],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02229789,0.0001458142,0.974724,0.0005033978,0.0004220837,0.0006338783,0.00004104421,0.0002280563,0.001003858],"genre_scores_gemma":[0.9755535,0.0001379328,0.02042921,0.00008435867,0.000175826,0.00001703678,0.0001372278,0.0000236314,0.003441304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9542948,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06194783974044825,"score_gpt":0.1896847681330857,"score_spread":0.1277369283926374,"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."}}