{"id":"W2849310602","doi":"10.1111/cgf.13418","title":"PixelSNE: Pixel‐Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen‐Resolution Precision","year":2018,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"National Research Foundation of Korea","keywords":"Embedding; Computer science; Visualization; Pixel; Scale (ratio); Range (aeronautics); Tree (set theory); Space (punctuation); Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Physics","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":[0.0003537907,0.0008425665,0.0004471283,0.0005974242,0.0002226661,0.0007002308,0.0007709541,0.0004328373,0.004074075],"category_scores_gemma":[0.002243695,0.0002734981,0.0003789325,0.0006044017,0.0002782015,0.001365086,0.001090968,0.0007113642,0.001070727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002734379,"about_ca_system_score_gemma":0.0004941725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001867087,"about_ca_topic_score_gemma":0.004188184,"domain_scores_codex":[0.9996367,0.00008791643,0.00001925365,0.0000552707,0.0001756399,0.0000252171],"domain_scores_gemma":[0.9994393,0.0002341374,0.00004447135,0.0001119022,0.0001365895,0.00003370221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004428632,0.000142897,0.001843251,0.0003674835,0.0001067943,0.0003077188,0.0003403701,0.3103234,0.06312972,0.02910879,0.02179842,0.5720884],"study_design_scores_gemma":[0.00001647488,0.00002816625,0.0001895959,0.000007485537,0.00000373058,0.00006218439,0.00002535386,0.9815568,0.009635277,0.005147417,0.00331461,0.00001298113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02175814,0.0001904531,0.9737197,0.0001236235,0.00006782764,0.00004795968,0.0003296351,0.002635886,0.001126755],"genre_scores_gemma":[0.2588861,0.0002708274,0.7351144,0.00009419755,0.00003362737,0.0001706622,0.00142601,0.0006031016,0.00340098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004074075,"threshold_uncertainty_score":0.01362914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824309092424188,"score_gpt":0.2992238744289965,"score_spread":0.2809807835047546,"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."}}