{"id":"W4226165407","doi":"10.1109/cogmi52975.2021.00013","title":"Impact Patterns of Combining Model Pruning and Continual Learning on Model Performance","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Pruning; Computer science; Machine learning; Artificial intelligence; Software deployment","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01394456,0.002255249,0.001485886,0.001616687,0.001239634,0.002406433,0.002476748,0.002574902,0.001016504],"category_scores_gemma":[0.06783739,0.0008665068,0.0009742513,0.001086683,0.001930271,0.005599712,0.002609815,0.0033641,0.0004423085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000904838,"about_ca_system_score_gemma":0.0016146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007256633,"about_ca_topic_score_gemma":0.008907944,"domain_scores_codex":[0.9916875,0.003295814,0.0006567502,0.00187973,0.001549077,0.0009311892],"domain_scores_gemma":[0.9368987,0.04734175,0.002111691,0.008299733,0.004004187,0.001343904],"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":[0.002399786,0.001255756,0.05520501,0.0007861241,0.001045445,0.0006158237,0.0006848912,0.6680266,0.01388179,0.004122273,0.005607713,0.2463688],"study_design_scores_gemma":[0.0001228857,0.001344187,0.008387811,0.0001962744,0.000300034,0.0004997256,0.0007322085,0.961859,0.01798603,0.00617296,0.002302708,0.00009622955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8927992,0.006134825,0.08898295,0.00218465,0.0004009434,0.0002368064,0.0004707488,0.003565512,0.0052243],"genre_scores_gemma":[0.9561788,0.0005167633,0.04138327,0.0002869049,0.00006834569,0.00007495905,0.0005415716,0.0003102593,0.0006390965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01394456,"threshold_uncertainty_score":0.0737468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559435759032219,"score_gpt":0.2689923622871192,"score_spread":0.243398004696797,"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."}}