{"id":"W4312093837","doi":"10.1162/neco_a_01560","title":"Dynamic Consolidation for Continual Learning","year":2022,"lang":"en","type":"article","venue":"Neural Computation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Consolidation (business); Artificial intelligence; Computer science; Machine learning; Cognitive science; Psychology; Economics","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":[0.00249969,0.001302745,0.00176292,0.00124411,0.00074193,0.001621493,0.003865658,0.001589629,0.003259326],"category_scores_gemma":[0.008279652,0.000887088,0.001015206,0.00105689,0.002315874,0.004822917,0.004498412,0.003612522,0.0008705076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235541,"about_ca_system_score_gemma":0.001341299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002962342,"about_ca_topic_score_gemma":0.003301861,"domain_scores_codex":[0.9990252,0.0002053369,0.00008005206,0.0003675078,0.0002154873,0.0001064484],"domain_scores_gemma":[0.9972628,0.001124465,0.0003040463,0.0007320199,0.0003865745,0.0001900163],"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":[0.0003049225,0.0003788851,0.003463309,0.0003214029,0.0001800261,0.0002524441,0.0003706996,0.5399142,0.008622704,0.05853736,0.005222761,0.3824313],"study_design_scores_gemma":[0.00001041595,0.00004085202,0.0001444049,0.00001722185,0.000012169,0.00003890457,0.00001625526,0.9767655,0.001240918,0.02065464,0.001048273,0.00001054244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02148233,0.001025245,0.9738026,0.0003935226,0.00008320164,0.00006630174,0.00006886511,0.001192218,0.001885798],"genre_scores_gemma":[0.7858809,0.0008866304,0.2066015,0.0006773091,0.0002517465,0.0003557502,0.0004628671,0.000343707,0.004539549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003865658,"threshold_uncertainty_score":0.01321977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977170686320211,"score_gpt":0.2824702065079763,"score_spread":0.2626984996447742,"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."}}