{"id":"W2587976075","doi":"10.1109/ssci.2016.7850122","title":"Biasing restricted Boltzmann machines using Gaussian filters to learn invariant visual features","year":2016,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Gaussian filter; Computer science; Gaussian; Pattern recognition (psychology); Unsupervised learning; Preprocessor; Boltzmann machine; Restricted Boltzmann machine; Invariant (physics); Feature extraction; Gaussian blur; Filter (signal processing); Computer vision; Deep learning; Image processing; Mathematics; Image (mathematics); Image restoration","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001418351,0.0009898386,0.001119078,0.0004824713,0.0002756731,0.0008133949,0.001605836,0.001211852,0.001256791],"category_scores_gemma":[0.0053047,0.0006254424,0.001108797,0.000506452,0.001055549,0.001686634,0.001111201,0.001960933,0.0004773831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001238623,"about_ca_system_score_gemma":0.000813857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004451219,"about_ca_topic_score_gemma":0.005049092,"domain_scores_codex":[0.9994692,0.0001649722,0.00002539779,0.0001500023,0.0001033907,0.0000870151],"domain_scores_gemma":[0.9983568,0.0009202639,0.0001708188,0.0002717101,0.0002104843,0.00006993527],"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":[0.0001292011,0.00005930068,0.001020597,0.00005810763,0.00009260599,0.000041174,0.00005263564,0.9239976,0.007995522,0.007371448,0.0008124465,0.05836922],"study_design_scores_gemma":[0.000005200806,0.00001883671,0.00007838068,0.000002743642,0.000004430707,0.00000836401,0.000002862181,0.9938835,0.001452556,0.004432563,0.0001061083,0.000004570716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05243125,0.0002613621,0.9440811,0.0002118222,0.00005157314,0.00005524602,0.00007755746,0.001793625,0.001036445],"genre_scores_gemma":[0.8111892,0.0002314316,0.1843112,0.0003785893,0.00005943383,0.000225067,0.0003650697,0.0003420471,0.002897839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004451219,"threshold_uncertainty_score":0.00898695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283706878601409,"score_gpt":0.2695525309965295,"score_spread":0.2467154622105154,"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."}}