{"id":"W4383174182","doi":"10.48550/arxiv.2307.00504","title":"On efficient computation in active inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Canadian Institute for Advanced Research","keywords":"Computer science; Inference; Computation; Process (computing); Grid; Task (project management); Artificial intelligence; Reinforcement learning; State (computer science); Dynamic programming; Mathematical optimization; Computational complexity theory; Machine learning; Algorithm; Mathematics","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":[0.002845126,0.001166197,0.001323576,0.00102413,0.001011342,0.001775016,0.002567737,0.001561577,0.004705817],"category_scores_gemma":[0.01150317,0.0007511034,0.001055561,0.001444353,0.002453319,0.003434112,0.002938716,0.002932589,0.0009393615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001680673,"about_ca_system_score_gemma":0.001961452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006195344,"about_ca_topic_score_gemma":0.00745657,"domain_scores_codex":[0.998789,0.0005139566,0.00006807946,0.000226871,0.0002912408,0.0001108142],"domain_scores_gemma":[0.9937068,0.005113498,0.0002201051,0.0005281218,0.0003252935,0.0001062751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000145866,0.0000616679,0.0004323795,0.0001346345,0.00005115238,0.00008806081,0.0001204808,0.6165192,0.001232168,0.2928894,0.001967774,0.08635722],"study_design_scores_gemma":[0.00001524215,0.00001230298,0.00003796165,0.00001162968,0.000005956435,0.000009447239,0.000007516984,0.8789357,0.0003794307,0.1196181,0.0009612197,0.000005491655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002074461,0.0002016356,0.9948026,0.0002459489,0.00002825749,0.00002564411,0.00003000019,0.0002007872,0.00239066],"genre_scores_gemma":[0.2472951,0.0005860376,0.7464439,0.0003582258,0.0001265966,0.0004461859,0.0002385651,0.0002739098,0.004231508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006195344,"threshold_uncertainty_score":0.01574254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07750341264697344,"score_gpt":0.2263258582020775,"score_spread":0.148822445555104,"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."}}