{"id":"W3026390873","doi":"10.1145/3388538","title":"Delayed Rejection Metropolis Light Transport","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Japan Society for the Promotion of Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Markov chain; Benchmark (surveying); Ergodicity; Kernel (algebra); State space; Markov chain Monte Carlo; Sample (material); Algorithm; Sample space; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Bayesian probability","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":[0.002384473,0.0009247785,0.001330512,0.0006406246,0.0007310901,0.001565394,0.00331092,0.001991457,0.005560124],"category_scores_gemma":[0.01015888,0.0006606237,0.001008123,0.0005678991,0.001583511,0.001821369,0.001954607,0.002869429,0.001698763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657016,"about_ca_system_score_gemma":0.001933903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003736164,"about_ca_topic_score_gemma":0.004105772,"domain_scores_codex":[0.9989116,0.0003435509,0.00004868287,0.0002205362,0.0003271983,0.0001483479],"domain_scores_gemma":[0.9951625,0.002985678,0.0002752854,0.0007007296,0.0006153581,0.0002605545],"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":[0.0006391155,0.0002351562,0.002031107,0.0002083328,0.00007861623,0.0002670633,0.000291173,0.7679693,0.01663759,0.1065406,0.004084128,0.1010178],"study_design_scores_gemma":[0.00002490547,0.0000204331,0.00002875802,0.000003983108,0.000004452225,0.00001637711,0.000007853059,0.9897162,0.002350153,0.007165423,0.0006537028,0.000007789909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01727971,0.00008448021,0.9795123,0.0001784681,0.00005372404,0.0001085576,0.0000337134,0.001175768,0.001573238],"genre_scores_gemma":[0.4857245,0.000120796,0.5040559,0.0003136892,0.00005775567,0.0004913621,0.0001969053,0.000633589,0.008405633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005560124,"threshold_uncertainty_score":0.01860046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806391939420383,"score_gpt":0.2759656322744736,"score_spread":0.2479017128802698,"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."}}