{"id":"W2266004465","doi":"","title":"Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning .","year":2012,"lang":"en","type":"article","venue":"Cambridge University Engineering Department Publications Database","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian probability; Artificial intelligence; Unsupervised learning; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005058624,0.000127927,0.0001104337,0.0002674824,0.0002606864,0.0001152742,0.0003058885,0.00003733249,0.000008159192],"category_scores_gemma":[0.0002406573,0.0001511321,0.00004832669,0.0003701369,0.00002081375,0.001406493,0.0001816483,0.0000931906,0.00001370078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008836533,"about_ca_system_score_gemma":0.00004005884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007304358,"about_ca_topic_score_gemma":7.315863e-7,"domain_scores_codex":[0.999094,0.00004841963,0.000115683,0.0002953134,0.0001378606,0.000308721],"domain_scores_gemma":[0.9990264,0.0002091468,0.00005016262,0.0003917851,0.00007572443,0.00024685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002793894,0.0006582027,0.005676731,0.0002245755,0.0002525424,0.000006755775,0.0005606496,0.002132091,0.002824689,0.8543732,0.01038908,0.1228735],"study_design_scores_gemma":[0.0005720922,0.00003456194,0.004773192,0.00001736558,0.00005412744,0.00002241388,0.0001522931,0.8585699,0.001018415,0.000002196638,0.1344803,0.0003031716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03910385,0.0001177177,0.9586244,0.0004883409,0.00009421699,0.0003575942,0.00009077568,0.000357069,0.0007660632],"genre_scores_gemma":[0.4591235,0.00004151391,0.538672,0.00005898172,0.00009128838,0.00003991303,0.0007712985,0.00001909702,0.001182475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8564378,"threshold_uncertainty_score":0.6162987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08170086113538608,"score_gpt":0.2562536995249276,"score_spread":0.1745528383895415,"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."}}