{"id":"W2574090101","doi":"","title":"Distance-preserving probabilistic embeddings with side information: variational Bayesian multidimensional scaling Gaussian process","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Embedding; Markov chain Monte Carlo; Probabilistic logic; Leverage (statistics); Gaussian process; Dimensionality reduction; Bayesian probability; Artificial intelligence; Posterior probability; Nonlinear dimensionality reduction; Bayesian inference; Gaussian; Algorithm; Machine learning; Theoretical computer science","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":[0.000400992,0.000411614,0.0003045568,0.0003566334,0.0003040117,0.0007726395,0.001578538,0.0001234789,0.0006339811],"category_scores_gemma":[0.0005691685,0.0002793027,0.00009631376,0.0005199366,0.0002480526,0.003125705,0.0002663813,0.0002836332,0.0004624376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000215048,"about_ca_system_score_gemma":0.0005560268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004346884,"about_ca_topic_score_gemma":0.00006473502,"domain_scores_codex":[0.9962993,0.00006718829,0.0009610059,0.0007825948,0.00134327,0.000546693],"domain_scores_gemma":[0.9970297,0.0002675631,0.0005111423,0.0005795701,0.001346562,0.0002654121],"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":[0.0001064906,0.0001295836,0.0001282508,0.00003027437,0.00003436131,0.00002161945,0.0007243282,0.001582373,0.00068542,0.912053,0.00004259109,0.08446164],"study_design_scores_gemma":[0.0002367811,0.0002688258,0.001304374,0.00129246,0.0000129384,0.00008776497,0.000354756,0.4961033,0.01771294,0.4808748,0.0009022151,0.0008488018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002484831,0.00000459016,0.9704209,0.01545266,0.0006534982,0.0003394657,0.00003722179,0.0002351896,0.01037166],"genre_scores_gemma":[0.9603869,0.0000117095,0.03855186,0.0005783873,0.0001701077,0.0001117195,0.00001848519,0.00001707896,0.0001537307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9579021,"threshold_uncertainty_score":0.9999659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0351644273836407,"score_gpt":0.2815418654416019,"score_spread":0.2463774380579612,"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."}}