{"id":"W1794939664","doi":"10.48550/arxiv.1312.6171","title":"Learning Paired-associate Images with An Unsupervised Deep Learning Architecture","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"MNIST database; Computer science; Associative property; Artificial intelligence; Restricted Boltzmann machine; Unsupervised learning; Content-addressable memory; Representation (politics); Boltzmann machine; Deep learning; Pattern recognition (psychology); Modal; Artificial neural network; Machine learning; Mathematics","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":[0.000635557,0.0005458934,0.0004155302,0.0002992438,0.0002274128,0.0005903224,0.001208868,0.0008091319,0.001909796],"category_scores_gemma":[0.001631689,0.0003796771,0.0005924846,0.0003446031,0.0006763831,0.001439872,0.001154996,0.001173903,0.0005677785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004697767,"about_ca_system_score_gemma":0.0004144463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292814,"about_ca_topic_score_gemma":0.002404588,"domain_scores_codex":[0.9997339,0.00007019932,0.000009506806,0.000101696,0.00005561464,0.00002906992],"domain_scores_gemma":[0.99958,0.0001470424,0.00005132173,0.0001071156,0.00008453176,0.0000299843],"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":[0.0003101365,0.0003271791,0.003217071,0.0001307161,0.0002029845,0.0001906712,0.0002720217,0.5024809,0.05522643,0.02604159,0.003482613,0.4081178],"study_design_scores_gemma":[0.000004668261,0.00003585722,0.0001927593,0.000003878815,0.000009086701,0.00002711968,0.000009763885,0.9847211,0.006474167,0.008106253,0.0004095553,0.000005718353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06634902,0.0001325067,0.9291654,0.0001841114,0.00004119247,0.0000345114,0.000081817,0.001599683,0.002411739],"genre_scores_gemma":[0.7218894,0.00009533901,0.2725181,0.0001960114,0.00002903386,0.000089795,0.0001955103,0.00008816274,0.004898645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001909796,"threshold_uncertainty_score":0.006388903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870846419632405,"score_gpt":0.185583910849351,"score_spread":0.1568754466530269,"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."}}