{"id":"W2994029118","doi":"","title":"Machine Unlearning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Retraining; Machine learning; Artificial intelligence; Overhead (engineering); Process (computing); Stochastic gradient descent; Point (geometry); Artificial neural network","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":[0.002747788,0.002029722,0.001525772,0.0009641148,0.0008761737,0.002326404,0.00317837,0.001709402,0.01220255],"category_scores_gemma":[0.02018569,0.000735282,0.001474656,0.0009591175,0.001394624,0.00496921,0.003621605,0.004086732,0.006174748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171295,"about_ca_system_score_gemma":0.002453665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894184,"about_ca_topic_score_gemma":0.003440827,"domain_scores_codex":[0.996104,0.001236134,0.0002980173,0.001090889,0.0008733605,0.0003975619],"domain_scores_gemma":[0.9892397,0.00465016,0.0004152913,0.004276123,0.00120225,0.0002163993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004958858,0.0005191492,0.005139832,0.0006156039,0.0001879959,0.0002833439,0.000344757,0.1459089,0.004794721,0.06116279,0.04839402,0.7321531],"study_design_scores_gemma":[0.00005626337,0.0001457732,0.0003887597,0.00008545196,0.00003380221,0.0001811763,0.0001057454,0.8812727,0.006911141,0.08884528,0.02194241,0.00003135123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02209315,0.001449059,0.9432324,0.002006948,0.0006088151,0.0004892927,0.001096018,0.01341705,0.01560722],"genre_scores_gemma":[0.5100363,0.001569762,0.4480373,0.002988326,0.0006222729,0.00128324,0.007671267,0.00211847,0.02567309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01220255,"threshold_uncertainty_score":0.04082155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0765322803673119,"score_gpt":0.2031887619576954,"score_spread":0.1266564815903836,"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."}}