{"id":"W4322620966","doi":"10.1117/12.2672661","title":"Reinforcing feature distributions of hidden units of Boltzmann machine using correlations","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Boltzmann machine; Computer science; Randomness; Encoding (memory); Artificial intelligence; Probabilistic logic; Hebbian theory; Probability distribution; Information theory; Visual cortex; Feature (linguistics); Inference; Machine learning; Theoretical computer science; Artificial neural network; Mathematics; Psychology","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":[0.001920033,0.0004958334,0.0008695651,0.0007115429,0.0004661366,0.001000717,0.001402172,0.0008249627,0.001976989],"category_scores_gemma":[0.007884551,0.0006078783,0.001031574,0.0005197953,0.00175845,0.002489391,0.001192819,0.001361915,0.0002557732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034608,"about_ca_system_score_gemma":0.0008146546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002271145,"about_ca_topic_score_gemma":0.001655816,"domain_scores_codex":[0.9992133,0.0003306748,0.00003576905,0.000163926,0.0001604541,0.00009586648],"domain_scores_gemma":[0.9974942,0.001745618,0.0002155115,0.0002039004,0.0002317733,0.0001090641],"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":[0.00008560962,0.000032648,0.00202288,0.0001019679,0.00006551922,0.00008353474,0.000135773,0.7567284,0.003331534,0.2151155,0.0005566389,0.02173996],"study_design_scores_gemma":[0.00000470223,0.00001317189,0.0002103898,0.000007283707,0.000005940945,0.00001830806,0.000004831234,0.9540389,0.0003997541,0.04511337,0.0001739613,0.000009406366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03755542,0.0002157128,0.9600972,0.0002206159,0.00003314572,0.00003260316,0.00004734331,0.0001467424,0.001651194],"genre_scores_gemma":[0.8680468,0.0004560472,0.1275856,0.0001576823,0.00009688525,0.0002211445,0.0001183005,0.000159084,0.003158516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002271145,"threshold_uncertainty_score":0.01015419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05074138684249598,"score_gpt":0.277968649327968,"score_spread":0.227227262485472,"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."}}