{"id":"W2138806976","doi":"10.1111/j.1756-8765.2010.01109.x","title":"Discovering Binary Codes for Documents by Learning Deep Generative Models","year":2010,"lang":"en","type":"article","venue":"Topics in Cognitive Science","topic":"Topic Modeling","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generative model; Generative grammar; Artificial intelligence; Word (group theory); Binary number; Inference; Set (abstract data type); Code (set theory); Natural language processing; Filter (signal processing); Binary code; Associative property; Deep learning; Information retrieval; Machine learning; Mathematics; Arithmetic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001520653,0.001046692,0.001121528,0.004230149,0.0006134999,0.002133433,0.002171044,0.001585833,0.002849551],"category_scores_gemma":[0.01240783,0.0009714679,0.001644389,0.003340987,0.001172584,0.004739951,0.001725137,0.00271019,0.001779686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001760791,"about_ca_system_score_gemma":0.001130987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005664711,"about_ca_topic_score_gemma":0.007615492,"domain_scores_codex":[0.9988987,0.0003092633,0.00008059372,0.0003095094,0.0002833992,0.0001183978],"domain_scores_gemma":[0.9941451,0.004106648,0.0004661661,0.0006536948,0.0004809753,0.0001474757],"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":[0.0004553885,0.0003389128,0.01118807,0.000461903,0.0002276954,0.0004594909,0.0007875082,0.2456479,0.01231554,0.09721297,0.01468908,0.6162156],"study_design_scores_gemma":[0.00002005394,0.00002219528,0.0004705373,0.00002601199,0.00002655049,0.0001140198,0.00004255757,0.9198707,0.002382998,0.07565599,0.00134322,0.00002519198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0322032,0.0004274016,0.9633766,0.0004695272,0.00005053258,0.00007621924,0.0006848453,0.001697534,0.001014174],"genre_scores_gemma":[0.5034541,0.000942621,0.4855276,0.0003752471,0.0002193208,0.0003205963,0.004035402,0.0004735649,0.004651537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005664711,"threshold_uncertainty_score":0.01277554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03774018067284531,"score_gpt":0.3249539954884382,"score_spread":0.2872138148155929,"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."}}