{"id":"W4393404820","doi":"10.1109/tmlcn.2024.3384329","title":"Transfer Learning With Reconstruction Loss","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transfer of learning; Computer science; Transfer (computing); Artificial intelligence; Parallel computing","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":[0.004646742,0.00220107,0.001899423,0.001049865,0.0006223702,0.001591322,0.003151508,0.003577874,0.006727736],"category_scores_gemma":[0.01178594,0.0006138228,0.001204452,0.001340239,0.002122801,0.003888933,0.003770983,0.003645502,0.002235661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830862,"about_ca_system_score_gemma":0.001425783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001681466,"about_ca_topic_score_gemma":0.001001774,"domain_scores_codex":[0.9984288,0.0006396323,0.00009021349,0.000330489,0.0003683474,0.0001424475],"domain_scores_gemma":[0.9963303,0.002199554,0.0002309033,0.0006272375,0.0004791442,0.0001329316],"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":[0.0003131455,0.000214837,0.001097988,0.0002775702,0.0001436101,0.000211091,0.00007575851,0.741279,0.001586189,0.05116779,0.009576838,0.1940562],"study_design_scores_gemma":[0.00001660115,0.00006980577,0.00007948885,0.0000143122,0.00001111987,0.00003769393,0.000008157538,0.9732438,0.0008396881,0.02477808,0.0008939707,0.000007429208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009211695,0.0008226844,0.9836751,0.000668559,0.00009494543,0.0001019813,0.0001122432,0.001012628,0.004300188],"genre_scores_gemma":[0.7156341,0.001360499,0.2620289,0.0009877298,0.0004236921,0.0008667347,0.001116107,0.0004533703,0.01712883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006727736,"threshold_uncertainty_score":0.02457464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257898259596548,"score_gpt":0.2566127287957027,"score_spread":0.2340337461997373,"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."}}