{"id":"W2904464104","doi":"10.1609/aaai.v33i01.33013248","title":"Deep Convolutional Sum-Product Networks","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Convolutional neural network; Pooling; Computer science; Inference; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Probabilistic logic; Differentiable function; Benchmark (surveying); Algorithm; 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":[0.001722956,0.001263905,0.0007342485,0.0007062249,0.0004891992,0.001629992,0.001634175,0.00132166,0.005088537],"category_scores_gemma":[0.007137502,0.0005693357,0.001018836,0.0008026896,0.001525981,0.003342971,0.002739067,0.002660676,0.001108399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001951762,"about_ca_system_score_gemma":0.001528084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004074396,"about_ca_topic_score_gemma":0.006890698,"domain_scores_codex":[0.99884,0.0002261125,0.00008851964,0.0002677287,0.0004022964,0.0001753139],"domain_scores_gemma":[0.997811,0.0009458293,0.000260632,0.0003891717,0.0004592342,0.0001340976],"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":[0.0001414974,0.00006146733,0.00151867,0.0002190962,0.00007916238,0.0003116223,0.0001343962,0.4604266,0.007208701,0.4343841,0.008067143,0.08744749],"study_design_scores_gemma":[0.000006074838,0.00002817457,0.0001901326,0.00002239706,0.0000127793,0.0000892684,0.00001211747,0.856396,0.003056952,0.1368589,0.003315421,0.00001181293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0179331,0.0008670314,0.9687636,0.0005299066,0.0001045024,0.00005362872,0.0006593612,0.0006561896,0.01043269],"genre_scores_gemma":[0.7092113,0.002250294,0.2674407,0.0008299766,0.0001733363,0.0003040635,0.00283295,0.0005277298,0.01642968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005088537,"threshold_uncertainty_score":0.01702285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03965008146062683,"score_gpt":0.2524805903209098,"score_spread":0.212830508860283,"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."}}