{"id":"W2996891787","doi":"10.1609/aaai.v34i04.6038","title":"Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Beijing Advanced Innovation Center for Big Data and Brain Computing; National Natural Science Foundation of China","keywords":"Learning vector quantization; Competitive learning; Artificial intelligence; Artificial neural network; Computer science; Vector quantization; Information bottleneck method; Machine learning; Quantization (signal processing); Feature learning; Instance-based learning; Classifier (UML); Pattern recognition (psychology); Unsupervised learning; Algorithm; Cluster analysis","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":[0.003399865,0.0008315353,0.001903201,0.001440543,0.00052261,0.001805854,0.003106049,0.001428697,0.002170376],"category_scores_gemma":[0.008592124,0.0005832704,0.0008129366,0.001935148,0.001422312,0.004150791,0.002743279,0.002295689,0.0004308139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306448,"about_ca_system_score_gemma":0.0009955519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002836203,"about_ca_topic_score_gemma":0.002132224,"domain_scores_codex":[0.9981685,0.0007324562,0.0001176334,0.0003606794,0.0004969308,0.0001237573],"domain_scores_gemma":[0.9972298,0.001405054,0.0002506454,0.0004250918,0.0005719706,0.0001175795],"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":[0.0001138527,0.0001297097,0.001099897,0.0002647906,0.0001610427,0.00008808808,0.0002723908,0.6233183,0.003136809,0.1474712,0.003200369,0.2207436],"study_design_scores_gemma":[0.000005143869,0.00002981261,0.0000711394,0.00001112961,0.000009600156,0.00001227697,0.00001063889,0.9362646,0.0004587941,0.06249909,0.0006210978,0.000006738218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003688898,0.0004604956,0.9948259,0.0001618678,0.00004548471,0.00002801387,0.00004106287,0.0001323356,0.0006159073],"genre_scores_gemma":[0.4941972,0.001368812,0.4993535,0.0004424573,0.0004555404,0.0003355179,0.0004568739,0.0001500812,0.003239965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003399865,"threshold_uncertainty_score":0.0179804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1110966483058207,"score_gpt":0.280084446335612,"score_spread":0.1689877980297912,"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."}}