{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000908884,0.00049259,0.0008612207,0.0004314297,0.0004269701,0.0007384011,0.001394932,0.001327943,0.003419118],"category_scores_gemma":[0.002247042,0.0003953222,0.0005716882,0.000739115,0.0005300109,0.0009391525,0.001164567,0.001489019,0.001197019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005207597,"about_ca_system_score_gemma":0.001410573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009844551,"about_ca_topic_score_gemma":0.008631218,"domain_scores_codex":[0.9994398,0.00008329839,0.00002813376,0.0001712879,0.0002244409,0.00005304674],"domain_scores_gemma":[0.9993393,0.0002523348,0.00004283074,0.00007862281,0.0002561406,0.00003077666],"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":[0.0002782803,0.0001470964,0.001000592,0.0001770645,0.000128923,0.0001015324,0.00008671518,0.3539329,0.01928985,0.02814747,0.006504693,0.5902048],"study_design_scores_gemma":[0.000009973761,0.00001836782,0.0001469139,0.000004080832,0.00001108291,0.00002364713,0.000002047087,0.9959226,0.001541229,0.001352158,0.0009607153,0.000007251258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001974747,0.0001169956,0.9965085,0.00006856253,0.00009153715,0.00001333631,0.00002633784,0.0003875574,0.0008123485],"genre_scores_gemma":[0.2573441,0.000426994,0.7300272,0.0003205084,0.0002422081,0.0001440802,0.0003912011,0.0001661381,0.01093758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009844551,"threshold_uncertainty_score":0.01957446,"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."}}