{"id":"W1964812476","doi":"10.1109/taslp.2014.2303296","title":"Application of Deep Belief Networks for Natural Language Understanding","year":2014,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":460,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep belief network; Artificial intelligence; Computer science; Support vector machine; Boosting (machine learning); Artificial neural network; Machine learning; Deep learning; Principle of maximum entropy; Backpropagation; Pattern recognition (psychology)","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":[0.001467631,0.0008372595,0.0005063832,0.00112688,0.0003403481,0.001424129,0.0008660565,0.001080527,0.002071298],"category_scores_gemma":[0.005941058,0.0004164355,0.0004685527,0.0008515335,0.0006530804,0.002166464,0.001061917,0.001967009,0.0004080087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165906,"about_ca_system_score_gemma":0.0008302744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005178678,"about_ca_topic_score_gemma":0.005768038,"domain_scores_codex":[0.9993916,0.0002646251,0.00003207752,0.0001036293,0.0001642387,0.00004378518],"domain_scores_gemma":[0.9975562,0.001823908,0.0001299791,0.000143322,0.0002952041,0.00005140122],"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":[0.0001207126,0.0001123558,0.001514209,0.0001787109,0.0001139124,0.00009602914,0.0001754312,0.7213684,0.00347487,0.03170686,0.002523675,0.2386149],"study_design_scores_gemma":[0.000003465243,0.000008684933,0.0000965917,0.00001012003,0.000005369392,0.0000083519,0.00001037058,0.9750819,0.0007594979,0.02341662,0.0005949557,0.000004077458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02473487,0.002049386,0.9656041,0.001562424,0.00008012944,0.00004374071,0.0002117808,0.001372492,0.004341049],"genre_scores_gemma":[0.7466159,0.001430802,0.2483276,0.0003676456,0.0001078905,0.00008036385,0.000411141,0.0001192531,0.002539478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005178678,"threshold_uncertainty_score":0.01029712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281846356590224,"score_gpt":0.2571697326409619,"score_spread":0.2443512690750596,"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."}}