{"id":"W4404573441","doi":"10.48550/arxiv.2411.11983","title":"The occlusion process: improving sampler performance with parallel computation and variational approximation","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; UK Research and Innovation","keywords":"Computation; Process (computing); Computer science; Mathematical optimization; Mathematics; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005268241,0.0009502582,0.001239148,0.0009933913,0.0009504288,0.00144223,0.002552584,0.001327489,0.002676633],"category_scores_gemma":[0.02606072,0.001001967,0.0007856596,0.001383019,0.001361115,0.002132598,0.002315634,0.001960044,0.0007949853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001670627,"about_ca_system_score_gemma":0.003337343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01640035,"about_ca_topic_score_gemma":0.01782095,"domain_scores_codex":[0.997955,0.0008433685,0.0001001425,0.000275521,0.0006384258,0.000187634],"domain_scores_gemma":[0.9920477,0.005284979,0.0003918531,0.001379153,0.0006606117,0.0002357512],"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":[0.0003058738,0.0001662338,0.005200276,0.0001031839,0.0001130955,0.0001610185,0.0002713758,0.7702266,0.004780735,0.0615871,0.002694625,0.1543899],"study_design_scores_gemma":[0.00001823899,0.000008793755,0.00006994595,0.000003467379,0.00000542554,0.00001195131,0.000004921276,0.9925913,0.0005245826,0.006381876,0.000376117,0.000003367086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01423743,0.0001828499,0.9826412,0.0001829167,0.00003421382,0.00005339368,0.00002770641,0.001212732,0.001427537],"genre_scores_gemma":[0.3527403,0.0002287376,0.6441895,0.0001850193,0.0001016217,0.000209715,0.0001816119,0.000645865,0.001517697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01640035,"threshold_uncertainty_score":0.03260976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03453653353446975,"score_gpt":0.1930668477495553,"score_spread":0.1585303142150856,"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."}}