{"id":"W1544983288","doi":"10.48550/arxiv.1301.3568","title":"Joint Training Deep Boltzmann Machines for Classification","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Boltzmann machine; Inference; Computer science; Artificial intelligence; Train; Joint (building); Training (meteorology); Machine learning; Restricted Boltzmann machine; Generative grammar; Deep learning; Layer (electronics); Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000283355,0.0003253953,0.0003756453,0.0001899041,0.0002598491,0.0002631618,0.001212831,0.0002467802,0.00003699368],"category_scores_gemma":[0.00007250092,0.0003446348,0.0003040082,0.0002652928,0.00008222416,0.0005203253,0.000764869,0.0003098759,0.00006166708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001224304,"about_ca_system_score_gemma":0.0001256181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009315883,"about_ca_topic_score_gemma":0.00003714911,"domain_scores_codex":[0.9980702,0.0001387088,0.0002431972,0.001090129,0.00007441903,0.0003833142],"domain_scores_gemma":[0.99819,0.0001302837,0.0003195822,0.0009454181,0.0002639001,0.0001507952],"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":[0.00002661077,0.0001193068,0.000210184,0.0001181261,0.0002529602,0.00002919835,0.001336882,0.634901,0.001453211,0.3133718,0.003043857,0.04513684],"study_design_scores_gemma":[0.000264284,0.00003470384,0.0009589292,0.0000378916,0.00005034085,0.00000123698,0.0001000344,0.9326228,0.0001744413,0.06415983,0.001230281,0.000365208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01023997,0.00007657633,0.9853442,0.0004035531,0.0009444879,0.0005777339,0.00001218497,0.0001824514,0.002218829],"genre_scores_gemma":[0.963259,0.00006571683,0.0348755,0.0001370238,0.0003137917,0.000009980417,0.00003355514,0.00002231186,0.001283067],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9530191,"threshold_uncertainty_score":0.9999006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1554936460588441,"score_gpt":0.2034719326950399,"score_spread":0.04797828663619574,"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."}}