{"id":"W4392931151","doi":"10.1016/j.knosys.2024.111653","title":"Approximate and Memorize (A&M) : Settling opposing views in replay-based continuous unsupervised domain adaptation","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Memorization; Stability (learning theory); Computer science; Adaptation (eye); Forgetting; Scalability; Artificial intelligence; Domain (mathematical analysis); Machine learning; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002956573,0.0003408989,0.0004842932,0.0006764248,0.0003150913,0.0009688011,0.0004796302,0.0001267062,0.00002106361],"category_scores_gemma":[0.0001238678,0.00033835,0.0001122704,0.001182191,0.00006652147,0.0004304076,0.00009778444,0.0004184716,0.00007292736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002850758,"about_ca_system_score_gemma":0.0005028811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004949789,"about_ca_topic_score_gemma":0.00007271889,"domain_scores_codex":[0.9964966,0.0008484101,0.0008057416,0.0009126223,0.0004311489,0.0005055041],"domain_scores_gemma":[0.9982873,0.0006345111,0.0001755845,0.000604741,0.0001118959,0.0001859999],"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":[0.0002976107,0.000758917,0.004178001,0.006424153,0.0001593201,0.0005501144,0.07237327,0.6736805,0.04107191,0.09107743,0.002039213,0.1073896],"study_design_scores_gemma":[0.001228934,0.00007740829,0.00007387964,0.0006999205,0.00001223877,0.00001998873,0.0009076081,0.9591472,0.0001222324,0.0002388791,0.03708152,0.0003902681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03826777,0.007242692,0.9473401,0.000398499,0.00161228,0.0008872932,0.00001115362,0.0007306478,0.003509589],"genre_scores_gemma":[0.947084,0.00001012071,0.05158474,0.0001681456,0.0001158412,0.0002471547,0.00005098452,0.00006002144,0.0006790098],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9088162,"threshold_uncertainty_score":0.9999068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05531971084796615,"score_gpt":0.2890656279337792,"score_spread":0.233745917085813,"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."}}