{"id":"W4388726194","doi":"10.1109/iecon51785.2023.10311616","title":"Investigating Continual Learning Strategies in Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Dilemma; Memory consolidation; Machine learning; Stability (learning theory); Consolidation (business); Regularization (linguistics); Deep learning; Recurrent neural network; Psychology; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.000514643,0.00008973261,0.0001059705,0.0001473366,0.0001210846,0.000388676,0.0003159005,0.00004264738,0.00002287285],"category_scores_gemma":[0.0001391268,0.00008659477,0.0000275284,0.001017064,0.00003633199,0.0006546812,0.0001619019,0.0003302456,0.00007487865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001245664,"about_ca_system_score_gemma":0.00003418203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005124067,"about_ca_topic_score_gemma":0.00004980144,"domain_scores_codex":[0.9989359,0.0001346062,0.0002021266,0.0002393121,0.0001642993,0.0003237398],"domain_scores_gemma":[0.9995122,0.0002078989,0.00005851647,0.000125662,0.0000259949,0.00006975763],"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":[7.369632e-7,0.000004595901,0.01740089,0.000003779401,0.000002817965,0.00002506879,0.002613611,0.8416557,0.0002030753,0.0883562,0.0002975559,0.04943603],"study_design_scores_gemma":[0.0001881933,0.00002247422,0.03413681,0.00000964462,3.856285e-7,0.000002790145,0.002337486,0.9615776,0.000008916773,0.0007879358,0.0008219999,0.0001057633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3292731,0.00005094507,0.6383991,0.001050799,0.0003620913,0.0001115293,5.881915e-8,0.001451034,0.02930127],"genre_scores_gemma":[0.9910092,0.000003872471,0.007143972,0.0003307953,0.00004499672,0.000006646896,0.000003204932,0.000007459614,0.001449905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.661736,"threshold_uncertainty_score":0.3748011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970959480083419,"score_gpt":0.2731757162923119,"score_spread":0.2434661214914777,"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."}}