{"id":"W2912990207","doi":"","title":"Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation","year":2018,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Transformation (genetics); Artificial intelligence; Feature (linguistics); Adaptation (eye); Pattern recognition (psychology); Domain (mathematical analysis); Mathematics","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":[0.0005894501,0.000748947,0.001150558,0.0007815924,0.0004270503,0.000620002,0.001273581,0.0008452933,0.001702179],"category_scores_gemma":[0.001978491,0.0003345116,0.001090177,0.001235535,0.000551783,0.001345935,0.001990649,0.001378417,0.001036251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002692067,"about_ca_system_score_gemma":0.0005529388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002142764,"about_ca_topic_score_gemma":0.003016614,"domain_scores_codex":[0.9994215,0.0001582119,0.00002194355,0.0002208683,0.0001085456,0.00006899615],"domain_scores_gemma":[0.999305,0.0002158494,0.00004533978,0.0002623614,0.0001230042,0.00004837782],"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":[0.0005469417,0.0005115316,0.002519233,0.0001909674,0.000318691,0.0003397833,0.0002282108,0.1859633,0.06896394,0.01222246,0.01463516,0.7135598],"study_design_scores_gemma":[0.00001606681,0.00004847897,0.0005962712,0.000005328242,0.00002777613,0.0001297983,0.00003859184,0.9806445,0.006873615,0.009761712,0.001842951,0.00001494177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01957709,0.0002573263,0.977683,0.00008877495,0.0000656613,0.00003649173,0.0001396143,0.001161627,0.0009903912],"genre_scores_gemma":[0.6108429,0.0004518839,0.378343,0.0004017086,0.0001299867,0.0001980919,0.002649581,0.0004596108,0.006523292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002142764,"threshold_uncertainty_score":0.005694389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507312202427669,"score_gpt":0.2443671450387853,"score_spread":0.2192940230145086,"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."}}