{"id":"W4318824050","doi":"10.1109/icdm54844.2022.00125","title":"Set2Box: Similarity Preserving Representation Learning for Sets","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Data Mining (ICDM)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Similarity (geometry); Computer science; ENCODE; Set (abstract data type); Representation (politics); Source code; Code (set theory); Theoretical computer science; Data mining; Artificial intelligence; Algorithm; Programming language","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":[0.001841348,0.001471965,0.001707816,0.001977034,0.0006158568,0.001935975,0.003401099,0.001543219,0.006299267],"category_scores_gemma":[0.007902052,0.0005405559,0.001302412,0.002386529,0.0008301297,0.004993812,0.003732454,0.002261827,0.002755461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261385,"about_ca_system_score_gemma":0.001373033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002978768,"about_ca_topic_score_gemma":0.003546347,"domain_scores_codex":[0.997896,0.0004641706,0.0001409278,0.0005168547,0.0008535833,0.0001284788],"domain_scores_gemma":[0.9980075,0.0006512413,0.0001645332,0.0007198547,0.0003757625,0.00008116497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004764116,0.0002694113,0.001152872,0.0003605175,0.0001350498,0.00009540867,0.0001959263,0.1314558,0.01137015,0.03318512,0.01995753,0.8013457],"study_design_scores_gemma":[0.0000421691,0.0001902481,0.000345619,0.000041051,0.00002214092,0.0001176953,0.00006444115,0.9538057,0.009758161,0.02858638,0.00698801,0.0000383542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0101291,0.0004462628,0.9835238,0.0001468456,0.00008366398,0.0001301044,0.0006953463,0.003657513,0.001187383],"genre_scores_gemma":[0.1739581,0.0005952276,0.8135942,0.0004643651,0.0001151405,0.0006323922,0.005885405,0.0004992759,0.004255933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006299267,"threshold_uncertainty_score":0.0210731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2345270794719642,"score_gpt":0.419734007872125,"score_spread":0.1852069284001608,"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."}}