{"id":"W2010084981","doi":"10.1109/nano.2011.6144315","title":"SET based Boltzmann machine and Hopfield neural networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Boltzmann machine; Restricted Boltzmann machine; Artificial neural network; Hopfield network; Computer science; Boltzmann constant; Probabilistic logic; Set (abstract data type); Stochastic neural network; Artificial intelligence; Statistical physics; Recurrent neural network; Physics; Quantum mechanics","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":[0.0005847823,0.0003224682,0.0005761165,0.0003675424,0.0003369308,0.000787765,0.001197567,0.0007908092,0.00162742],"category_scores_gemma":[0.001947862,0.0002549289,0.0004344608,0.0004674311,0.0007672496,0.001277305,0.0006028995,0.0008052514,0.0003602959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006257208,"about_ca_system_score_gemma":0.0005065295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355258,"about_ca_topic_score_gemma":0.001882568,"domain_scores_codex":[0.9996232,0.0001265605,0.00001662906,0.00004445142,0.0001648777,0.00002415487],"domain_scores_gemma":[0.9996426,0.0002125663,0.00002504935,0.00003815696,0.00006426219,0.00001745075],"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":[0.00006731237,0.00002882897,0.0003725144,0.00008762275,0.00005524011,0.00006943174,0.00008807919,0.7474829,0.004439015,0.1814482,0.0006699596,0.06519084],"study_design_scores_gemma":[0.00000713416,0.00001352223,0.00006227343,0.000007216659,0.000005942911,0.00002533412,0.000005492972,0.9541962,0.001074086,0.04351679,0.001075727,0.00001029583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01332843,0.0005274593,0.9812624,0.0001768961,0.00007296837,0.00002608518,0.00003206167,0.0002329157,0.004340775],"genre_scores_gemma":[0.6323582,0.000835955,0.3576563,0.0001392314,0.00007430718,0.0001482272,0.00008050491,0.00006992013,0.00863739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002355258,"threshold_uncertainty_score":0.005444229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999993857586292,"score_gpt":0.2297421418745838,"score_spread":0.1997422032987209,"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."}}