{"id":"W4414910353","doi":"10.1016/j.automatica.2025.112639","title":"A conditional invertible neural network-based particle filter","year":2025,"lang":"en","type":"article","venue":"Automatica","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Shanghai Academy of Spaceflight Technology","keywords":"Particle filter; Invertible matrix; Artificial neural network; Computation; Particle (ecology); Filter (signal processing); Conditional probability distribution; Key (lock)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001822021,0.0001131588,0.0001383398,0.00004551722,0.0002022704,0.000186166,0.0005516108,0.00005572243,0.000207234],"category_scores_gemma":[0.00005489186,0.0001016979,0.00006171544,0.0005147062,0.00006148525,0.0002453726,0.0001633053,0.0001244543,0.0001943864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001828247,"about_ca_system_score_gemma":0.00007048739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000740264,"about_ca_topic_score_gemma":0.000003463032,"domain_scores_codex":[0.9988347,0.00008590351,0.0002525722,0.0002757299,0.0002047655,0.000346323],"domain_scores_gemma":[0.9989229,0.0003349455,0.00004800883,0.0005660937,0.00004907181,0.00007896285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000119805,0.000155181,0.006214756,0.00003892409,0.00003738212,0.00003524039,0.00008922644,0.06704854,0.0002044819,0.4536568,0.455307,0.01720046],"study_design_scores_gemma":[0.0003297838,0.00002337009,0.008780663,0.00003984322,0.000007341576,0.000003401062,0.000002156376,0.9598302,0.0005122371,0.02115053,0.009208104,0.0001123248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03130355,0.0001488618,0.9515328,0.008817205,0.001033161,0.0001936768,0.000009271587,0.001135843,0.005825649],"genre_scores_gemma":[0.9457273,0.000001151622,0.04684486,0.006900249,0.00007216996,0.00002506691,0.00002453735,0.00000555828,0.0003990319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9144238,"threshold_uncertainty_score":0.4147122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01381176564843517,"score_gpt":0.2492460140916556,"score_spread":0.2354342484432205,"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."}}