{"id":"W4403864655","doi":"10.1109/ihmsc62065.2024.00031","title":"Enhancing Data Preprocessing using Positional Self-Attention Autoencoders","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preprocessor; Artificial intelligence; Data pre-processing; Pattern recognition (psychology); Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001059092,0.0006535299,0.0004111892,0.0004842447,0.0002291693,0.00057334,0.000659702,0.0005255472,0.001401936],"category_scores_gemma":[0.00426712,0.0002789023,0.0005059213,0.0004592398,0.0003109874,0.001105252,0.0006385788,0.0006531564,0.0005434448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002793406,"about_ca_system_score_gemma":0.0007004827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003483077,"about_ca_topic_score_gemma":0.005790927,"domain_scores_codex":[0.9996779,0.00006626839,0.00002403742,0.00008668651,0.0001024568,0.00004264498],"domain_scores_gemma":[0.9986964,0.0006784916,0.0001109827,0.0001799058,0.0003094644,0.0000248533],"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":[0.0003163838,0.0003310274,0.008515648,0.000266334,0.0002240863,0.0002506392,0.0002819152,0.2070658,0.08979137,0.004686305,0.002919132,0.6853514],"study_design_scores_gemma":[0.00001384325,0.0001262469,0.00725715,0.00001901145,0.00005750034,0.00007725364,0.00005008992,0.9541383,0.03452554,0.001643361,0.002071131,0.00002065798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1406953,0.0006697367,0.8524551,0.0003309214,0.000151158,0.00005212155,0.0001070265,0.00294736,0.002591216],"genre_scores_gemma":[0.8031163,0.0004274888,0.1925662,0.000332748,0.0001246828,0.00006390257,0.000365904,0.0001980626,0.002804631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003483077,"threshold_uncertainty_score":0.006925642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04328345059102858,"score_gpt":0.3194445845874818,"score_spread":0.2761611339964533,"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."}}