{"id":"W2951774898","doi":"10.48550/arxiv.1504.01684","title":"Large Margin Nearest Neighbor Embedding for Knowledge Representation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; York University; National Science Foundation","keywords":"Embedding; Margin (machine learning); Benchmark (surveying); Computer science; k-nearest neighbors algorithm; Relation (database); Representation (politics); Simple (philosophy); False positive paradox; Artificial intelligence; Theoretical computer science; Algorithm; Machine learning; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003258871,0.0003668464,0.0003916684,0.0002942476,0.0002284041,0.0001667929,0.00186962,0.0003216519,0.000009880523],"category_scores_gemma":[0.000124454,0.0004377169,0.0002792169,0.0008698759,0.00007067488,0.0007433701,0.002484246,0.0005475843,0.00005881763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002600471,"about_ca_system_score_gemma":0.0002053054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001686593,"about_ca_topic_score_gemma":0.00006816579,"domain_scores_codex":[0.9974541,0.0001524944,0.0002473422,0.001461568,0.00009683739,0.0005876477],"domain_scores_gemma":[0.9972343,0.0003238001,0.0003269387,0.00143238,0.0004200157,0.000262551],"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":[0.00009811508,0.0001684972,0.001651359,0.0001233367,0.00009579053,0.0001765346,0.0008120929,0.6392026,0.00002948382,0.3464177,0.009443675,0.001780853],"study_design_scores_gemma":[0.0008042978,0.00004952761,0.0002297601,0.00007720668,0.00003990089,0.000003155444,0.00009799273,0.8709745,0.00006190874,0.1226612,0.004560655,0.0004398239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03810664,0.0002053728,0.9567223,0.00009629338,0.00154925,0.0007491293,0.00004340232,0.0004142873,0.002113368],"genre_scores_gemma":[0.9865754,0.00008527722,0.01013079,0.00007581668,0.0002788565,0.00000735891,0.00007729254,0.00003791809,0.002731262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9484688,"threshold_uncertainty_score":0.9998075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1244031586691689,"score_gpt":0.261012721967495,"score_spread":0.1366095632983261,"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."}}