{"id":"W1795776692","doi":"10.1371/journal.pone.0124088","title":"Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Bundesministerium für Bildung und Forschung; China Scholarship Council; Deutsche Forschungsgemeinschaft; Canadian Institute for Advanced Research","keywords":"Algorithm; Pixel; Computer science; Nonlinear system; Inference; Artificial intelligence; Pattern recognition (psychology); Mathematics","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":[0.0008621226,0.0004702931,0.0005590903,0.0005673135,0.0002353689,0.0007969448,0.001195632,0.0009187843,0.002247256],"category_scores_gemma":[0.003643878,0.0003448839,0.0006928133,0.0009340103,0.001191627,0.001441587,0.001095222,0.002080921,0.0006142193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080842,"about_ca_system_score_gemma":0.000828261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003766752,"about_ca_topic_score_gemma":0.004408227,"domain_scores_codex":[0.9996902,0.00009495044,0.00001339968,0.00004902825,0.00011675,0.00003570165],"domain_scores_gemma":[0.9989348,0.0006294905,0.0001082641,0.0001672379,0.0001164205,0.00004372153],"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":[0.0001026076,0.00003972419,0.0005241871,0.0001354773,0.00003032295,0.0001127778,0.0001774907,0.6400956,0.01020047,0.2750706,0.002770021,0.07074077],"study_design_scores_gemma":[0.000003311175,0.000006622341,0.00005167324,0.000005756224,0.000002050275,0.00001778161,0.000005508937,0.961109,0.0007709797,0.03747872,0.0005434133,0.000005170348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005072657,0.00008989987,0.9936236,0.0001746891,0.0000122233,0.00001239675,0.0001121375,0.0001371419,0.0007653712],"genre_scores_gemma":[0.4549902,0.0008298785,0.5354799,0.0003996803,0.0001167045,0.0002158726,0.0008702532,0.0002416867,0.006855752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003766752,"threshold_uncertainty_score":0.007842064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0766875410619325,"score_gpt":0.2474714433505868,"score_spread":0.1707839022886543,"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."}}