{"id":"W2274603360","doi":"10.71781/10876","title":"Improving sampling, optimization and feature extraction in Boltzmann machines","year":2013,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Boltzmann machine; Sampling (signal processing); Computer science; Extraction (chemistry); Feature (linguistics); Restricted Boltzmann machine; Artificial intelligence; Data mining; Pattern recognition (psychology); Statistics; Machine learning; Mathematics; Chromatography; Chemistry; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001520461,0.0008175743,0.001542439,0.0007572319,0.0003753688,0.001365998,0.001386974,0.001241168,0.001533025],"category_scores_gemma":[0.007900719,0.0008322738,0.001112092,0.001008025,0.0009175215,0.001894164,0.00149612,0.001500304,0.0006162258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009938201,"about_ca_system_score_gemma":0.0009788222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005744603,"about_ca_topic_score_gemma":0.004699831,"domain_scores_codex":[0.9991409,0.0003075809,0.00005312026,0.0001793645,0.0002156163,0.0001034375],"domain_scores_gemma":[0.9982702,0.001178328,0.0001120411,0.0001708791,0.0002094633,0.00005924534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001405241,0.0000470626,0.001229292,0.0001590048,0.00007002724,0.000044822,0.0001174465,0.8075406,0.005578088,0.02158325,0.0009332367,0.1625567],"study_design_scores_gemma":[0.000005114833,0.0000179594,0.0001363632,0.000006642375,0.000004514367,0.00001357083,0.000006476741,0.9929346,0.0008271996,0.005603001,0.000439442,0.000005019928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01324926,0.0005670287,0.9849416,0.0001170264,0.00003561782,0.00002146714,0.00002303004,0.0003773172,0.0006677126],"genre_scores_gemma":[0.4925964,0.000942193,0.5007767,0.0002063393,0.0001287955,0.0002224742,0.0002588882,0.0003724868,0.004495783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005744603,"threshold_uncertainty_score":0.01142234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103416931475968,"score_gpt":0.2961940343017991,"score_spread":0.2751598649870395,"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."}}