{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001749934,0.001423547,0.001565667,0.0009347515,0.0004384335,0.001355067,0.002770329,0.001698183,0.005203596],"category_scores_gemma":[0.00562323,0.0008909632,0.001306663,0.001317073,0.00126053,0.00236838,0.00274847,0.004326501,0.001905073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250247,"about_ca_system_score_gemma":0.00110944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002486997,"about_ca_topic_score_gemma":0.003793304,"domain_scores_codex":[0.9987601,0.0004512423,0.0000648651,0.0002849216,0.0003106951,0.0001281383],"domain_scores_gemma":[0.9985052,0.0008578114,0.00009866763,0.0003143738,0.0001544817,0.00006941393],"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":[0.00009652543,0.00008518355,0.0007918928,0.0001255476,0.0001305446,0.0000600545,0.00008931929,0.6949511,0.003471299,0.1060473,0.0057258,0.1884254],"study_design_scores_gemma":[0.000004457747,0.000007943444,0.00003517107,0.000007305328,0.000005108571,0.00001236788,0.000002880543,0.9629448,0.0006886083,0.03513631,0.00114988,0.000005099805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001432555,0.000240484,0.9966689,0.0001414575,0.00004242699,0.0000166028,0.00005220953,0.0005347278,0.0008707528],"genre_scores_gemma":[0.2739334,0.0008130675,0.7124644,0.0005782538,0.0002554578,0.000465937,0.0007810228,0.0006755426,0.01003288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005203596,"threshold_uncertainty_score":0.01740777,"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."}}