{"id":"W2962687528","doi":"","title":"Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions","year":2013,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Boltzmann machine; Artificial intelligence; Convolution (computer science); Layer (electronics); Deep learning; Spike (software development); Deep belief network; Inpainting; Texture (cosmology); Parametric statistics; Pattern recognition (psychology); Convolutional neural network; Computer vision; Algorithm; Image (mathematics); Artificial neural network; Mathematics; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006427176,0.0001023215,0.0001048374,0.00003081319,0.0001790421,0.0001659228,0.0001199524,0.00003622623,0.00007321493],"category_scores_gemma":[0.00001196943,0.00006624604,0.00001530701,0.00009222626,0.00005969216,0.0005139858,0.0001335725,0.00007113364,0.00002080217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006125962,"about_ca_system_score_gemma":0.00002004814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001364084,"about_ca_topic_score_gemma":0.00006433295,"domain_scores_codex":[0.999321,0.00002491184,0.0000908645,0.0002791811,0.0001138959,0.0001701021],"domain_scores_gemma":[0.9995391,0.00004610339,0.00002115004,0.0001700937,0.0001225081,0.0001009847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003048748,0.0001996645,0.003991221,0.00003309938,0.000231508,0.00003425926,0.002090316,0.1969135,0.01324743,0.480562,0.01099794,0.2916686],"study_design_scores_gemma":[0.0001617529,0.00003493221,0.001728932,0.000008308806,0.000005255827,0.00002483503,0.00006079647,0.9947599,0.0000822031,0.002829842,0.000188215,0.0001150626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01352248,0.0004023027,0.9823331,0.001862616,0.00005062127,0.0001141992,3.83464e-7,0.00004461835,0.001669686],"genre_scores_gemma":[0.8235031,0.00002949862,0.1755342,0.0005506415,0.00005116266,0.000008626325,6.644602e-7,0.000004052067,0.0003181041],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8099806,"threshold_uncertainty_score":0.2701435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006063522580150985,"score_gpt":0.1671736004481501,"score_spread":0.1611100778679991,"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."}}