{"id":"W2963622136","doi":"","title":"Bidirectional Helmholtz machines","year":2016,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Canadian Institute for Advanced Research","funders":"","keywords":"Inference; Autoencoder; Generative model; Computer science; Helmholtz free energy; Fiducial inference; Algorithm; Artificial intelligence; Bhattacharyya distance; Generative grammar; Frequentist inference; Machine learning; Bayesian inference; Deep learning; Bayesian probability","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002263463,0.0001560828,0.0001205008,0.0001494268,0.0001562397,0.0001892283,0.0006899759,0.00004138312,0.001313322],"category_scores_gemma":[0.0002401715,0.0001026934,0.00007726262,0.0001187301,0.000044438,0.0004638291,0.0001734028,0.0001814066,0.0004012481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005249996,"about_ca_system_score_gemma":0.00004104996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005681995,"about_ca_topic_score_gemma":0.00001912596,"domain_scores_codex":[0.9987391,0.0001352183,0.0001804807,0.0003840422,0.0003694096,0.0001917917],"domain_scores_gemma":[0.9992752,0.0001706971,0.0001041593,0.0001864987,0.0001917519,0.00007169411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005071226,0.00008396833,0.0119682,0.000002017169,0.00009334309,0.00002315573,0.0001194418,0.002532681,0.03457057,0.367837,0.0009835538,0.5817354],"study_design_scores_gemma":[0.0008851466,0.0002213894,0.008293247,0.0001161826,0.000005629083,0.00003419424,0.00001097424,0.8824903,0.006077197,0.01414094,0.08726098,0.0004638595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003564092,0.00003664854,0.9153736,0.01929205,0.001407391,0.00006675394,0.0000101496,0.000235112,0.06001427],"genre_scores_gemma":[0.9814817,0.00006966601,0.007826474,0.000391778,0.0003466638,0.00001135471,0.00000639657,0.00001020909,0.009855716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9779177,"threshold_uncertainty_score":0.9995996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02976715096329061,"score_gpt":0.275138694609757,"score_spread":0.2453715436464664,"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."}}