{"id":"W4415367385","doi":"10.1109/isit63088.2025.11195367","title":"Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems","year":2025,"lang":"","type":"article","venue":"","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverse problem; Mutual information; Inverse; SIGNAL (programming language); Sampling (signal processing); Field (mathematics); Posterior probability; Noise (video)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001977702,0.0006334306,0.0008260055,0.0006876465,0.0007833507,0.0004787576,0.0005161288,0.0005417328,0.001953913],"category_scores_gemma":[0.005400219,0.000627421,0.0004695587,0.0007500614,0.0003030272,0.001375332,0.0004351586,0.0005300848,0.0002151153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005412248,"about_ca_system_score_gemma":0.000650254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004190845,"about_ca_topic_score_gemma":0.0000173056,"domain_scores_codex":[0.9955724,0.000206889,0.002106179,0.0005785703,0.0006402758,0.0008956533],"domain_scores_gemma":[0.9923534,0.005097801,0.000834356,0.0006697656,0.0008058829,0.0002387554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001875859,0.002501373,0.00136151,0.03524318,0.00114197,0.000003829774,0.007127914,0.008426303,0.06190504,0.6609709,0.05017239,0.1692697],"study_design_scores_gemma":[0.004690296,0.0003374542,0.00006754583,0.00174894,0.0003730784,0.000005384489,0.001264611,0.6001267,0.005859827,0.342295,0.04245333,0.0007778261],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006390384,0.00004213139,0.9780401,0.001769212,0.002056675,0.003858252,0.0003111286,0.0002801649,0.007251976],"genre_scores_gemma":[0.03012103,0.00001895422,0.9612277,0.005162101,0.0001616412,0.0006932965,0.0003476201,0.00006173529,0.002205912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5917004,"threshold_uncertainty_score":0.9996177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0747983142666701,"score_gpt":0.3586080617699879,"score_spread":0.2838097475033178,"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."}}