{"id":"W2032676284","doi":"10.1016/j.neunet.2009.10.009","title":"Temporal-Kernel Recurrent Neural Networks","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Computer science; Connectionism; Artificial intelligence; Task (project management); String (physics); Kernel (algebra); Term (time); Sequence (biology); Recall; Machine learning; Artificial neural network; Mathematics","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":[0.0008140416,0.0004135496,0.0005893953,0.0002807945,0.0002209992,0.000783435,0.0007963462,0.0007569098,0.003083308],"category_scores_gemma":[0.003535325,0.0002177532,0.0003789454,0.0005233345,0.0002951428,0.001758117,0.0006008046,0.000783575,0.001020231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004137961,"about_ca_system_score_gemma":0.0004236087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346901,"about_ca_topic_score_gemma":0.003210222,"domain_scores_codex":[0.999713,0.00006065762,0.00002626472,0.00008169307,0.00007829665,0.00004009893],"domain_scores_gemma":[0.9989678,0.0003290338,0.0001259583,0.0002061027,0.0003317843,0.0000392832],"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":[0.0005069401,0.0001730803,0.002696606,0.0002358957,0.0001793721,0.0002079851,0.00009373204,0.4202191,0.02286525,0.06361149,0.00811687,0.4810936],"study_design_scores_gemma":[0.000003367363,0.00001368876,0.0001774513,0.000003167294,0.00001406373,0.00002101684,0.000004169719,0.9928228,0.001365574,0.005012955,0.0005568842,0.000004874946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05027859,0.001513877,0.9433067,0.0002732926,0.0002421416,0.00002583031,0.0002096073,0.0009882398,0.003161689],"genre_scores_gemma":[0.8809822,0.0009985315,0.1041686,0.0001231709,0.0001322267,0.00003772528,0.0006038808,0.0001858094,0.01276798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003083308,"threshold_uncertainty_score":0.0103147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770525029920036,"score_gpt":0.2562555405495667,"score_spread":0.2385502902503663,"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."}}