{"id":"W4313220191","doi":"10.1016/j.inffus.2022.12.023","title":"Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Centers for Mechanical Engineering Research and Education, Massachusetts Institute of Technology; Basic and Applied Basic Research Foundation of Guangdong Province; China Postdoctoral Science Foundation; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China; Massachusetts Institute of Technology","keywords":"Computer science; Artificial intelligence; Classifier (UML); Machine learning; Pattern recognition (psychology); Benchmark (surveying); Domain adaptation","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":[0.003087206,0.001349328,0.00204642,0.00165159,0.0008199977,0.001256049,0.001963972,0.001966135,0.003570697],"category_scores_gemma":[0.004999435,0.0006509992,0.002013893,0.001804616,0.0008666964,0.002221473,0.002887303,0.002308334,0.001781676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005137356,"about_ca_system_score_gemma":0.001112694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005619274,"about_ca_topic_score_gemma":0.007049973,"domain_scores_codex":[0.9985439,0.0005058036,0.00008337275,0.0004658405,0.0002187327,0.0001824178],"domain_scores_gemma":[0.9977962,0.001242841,0.00007687073,0.000407156,0.0003852344,0.00009172197],"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":[0.0006324144,0.0004216427,0.00152351,0.00011404,0.00040151,0.0001894844,0.0002580374,0.1628476,0.01502451,0.007069974,0.005474831,0.8060424],"study_design_scores_gemma":[0.00001323339,0.000074799,0.0006585954,0.00001254833,0.00006104206,0.00004533142,0.00003893607,0.9844437,0.00459396,0.009114423,0.000919947,0.00002347804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01799305,0.000737525,0.9779374,0.0001976482,0.0001085491,0.00005439838,0.0002156549,0.001618143,0.001137589],"genre_scores_gemma":[0.6298835,0.0006414952,0.3599251,0.0004515373,0.0002852621,0.0002844002,0.002426861,0.0004171515,0.005684751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005619274,"threshold_uncertainty_score":0.0163269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018607380328232,"score_gpt":0.2766555817169442,"score_spread":0.2264695079136618,"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."}}