{"id":"W4396600247","doi":"10.48550/arxiv.2404.19132","title":"Integrating Present and Past in Unsupervised Continual Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Unsupervised learning; Computer science; Artificial intelligence; Data science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004162289,0.000795349,0.0008442944,0.0009242666,0.0006266079,0.001850161,0.002493355,0.001301978,0.001612383],"category_scores_gemma":[0.01275627,0.0005408414,0.0006931032,0.0009677994,0.002571845,0.00535485,0.003834035,0.002562857,0.0004045158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009689766,"about_ca_system_score_gemma":0.001218747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001873268,"about_ca_topic_score_gemma":0.003255201,"domain_scores_codex":[0.9987483,0.0004116372,0.00008211358,0.0004490401,0.0002087309,0.0001001174],"domain_scores_gemma":[0.9937047,0.003360867,0.0007033956,0.001272575,0.0005955592,0.000362751],"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":[0.0003845836,0.0003908469,0.008490298,0.0002393335,0.0001427399,0.0001548731,0.0006194987,0.6860723,0.006429968,0.04375123,0.001507727,0.2518165],"study_design_scores_gemma":[0.000009124514,0.00009650721,0.0005374123,0.00001614436,0.0000108034,0.00003467401,0.00003510212,0.9550887,0.001187255,0.04233283,0.0006313918,0.00002006493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09783121,0.0005631926,0.8963771,0.0007873198,0.00006567509,0.00007562574,0.0001733903,0.0008626248,0.003263775],"genre_scores_gemma":[0.8889845,0.0002033518,0.1079137,0.0001763775,0.00008166979,0.0001488601,0.0002709797,0.0001143781,0.002106162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004162289,"threshold_uncertainty_score":0.02201259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0542853103253399,"score_gpt":0.1918385128842502,"score_spread":0.1375532025589103,"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."}}