{"id":"W3088543493","doi":"10.48550/arxiv.2009.10301","title":"Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Embedding; Gaussian; Nonlinear dimensionality reduction; Probabilistic logic; Probability distribution; Dimensionality reduction; Cover (algebra); Space (punctuation); Computer science; Manifold (fluid mechanics); Cauchy distribution; Mathematics; Statistical physics; Artificial intelligence; Physics; Mathematical analysis; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001582293,0.0002295193,0.0002528891,0.00009401564,0.0002019255,0.0002711035,0.0004673529,0.00014874,0.000007938776],"category_scores_gemma":[0.00005020025,0.0002216647,0.00003660173,0.000286737,0.0001010412,0.0003456445,0.001342681,0.0003595383,0.0000125548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005619071,"about_ca_system_score_gemma":0.0001004827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001549208,"about_ca_topic_score_gemma":0.0000659771,"domain_scores_codex":[0.9985573,0.0001441808,0.000121291,0.0008681162,0.00009505373,0.0002140746],"domain_scores_gemma":[0.9990338,0.0001512534,0.0001356518,0.0003634094,0.00009567749,0.0002201903],"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":[0.003096552,0.001744898,0.2210776,0.001751603,0.002672353,0.00448144,0.01121746,0.1941825,0.001569275,0.5393667,0.006506688,0.01233296],"study_design_scores_gemma":[0.004200486,0.0005680594,0.1693702,0.0009656738,0.0003329938,0.00003540505,0.0006265989,0.7867361,0.0001948699,0.03477565,0.0002517632,0.001942232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3898717,0.00003895515,0.6092322,0.000101232,0.0003165307,0.0001976422,0.00007677048,0.00009285196,0.00007208475],"genre_scores_gemma":[0.9986966,0.00006987197,0.000972515,0.00003283082,0.00008290257,0.000001399468,0.00007247725,0.000009147363,0.00006225095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6088249,"threshold_uncertainty_score":0.9039225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06702951026061885,"score_gpt":0.2160753167953538,"score_spread":0.149045806534735,"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."}}