{"id":"W2884092934","doi":"10.1613/jair.1.12105","title":"General Value Function Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Institut de Valorisation des Données; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Recurrent neural network; Computer science; Function (biology); Truncation (statistics); State (computer science); Observable; Artificial intelligence; Construct (python library); Domain (mathematical analysis); Machine learning; Algorithm; Artificial neural network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004223983,0.00008607807,0.0001693769,0.0003162117,0.0002934029,0.0005362507,0.0006597702,0.0000908417,0.0001747697],"category_scores_gemma":[0.0006708415,0.00007781346,0.0001343202,0.001405301,0.00009819981,0.0005789847,0.000193669,0.001021118,0.0001347339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009553241,"about_ca_system_score_gemma":0.0004819508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001290988,"about_ca_topic_score_gemma":0.000008733966,"domain_scores_codex":[0.9968963,0.0006657639,0.0005856759,0.0002212921,0.001206341,0.0004245908],"domain_scores_gemma":[0.9970327,0.0004054684,0.0001696619,0.0002903167,0.001885565,0.0002163031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006261618,0.0001376044,0.00006410803,0.000005847025,0.00003354204,0.0001973912,0.0005751959,0.07556501,0.009679525,0.4210621,0.002552201,0.4900648],"study_design_scores_gemma":[0.00005721169,0.000491923,0.0003064297,0.00006198348,0.000007232303,0.0002209656,0.00138998,0.8624622,0.03454672,0.07393416,0.02634544,0.0001757236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008458901,0.0008142603,0.9844244,0.00169633,0.002710748,0.00004711763,1.272062e-7,0.00001851184,0.001829603],"genre_scores_gemma":[0.9616539,0.0004408509,0.03180028,0.0003582344,0.004047876,0.000002885552,0.000001119951,0.00001627605,0.001678532],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.953195,"threshold_uncertainty_score":0.5171078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1737848302351369,"score_gpt":0.4116147020746685,"score_spread":0.2378298718395316,"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."}}