{"id":"W2554828302","doi":"10.1109/tpami.2016.2630686","title":"Local Submodularization for Binary Pairwise Energies","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; École de Technologie Supérieure; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Submodular set function; Pairwise comparison; Regularization (linguistics); Inpainting; Binary number; Computer science; Prior probability; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001134996,0.0001694146,0.0001987629,0.000299782,0.0002194009,0.00005092223,0.00035747,0.00005071125,0.00003144607],"category_scores_gemma":[0.000003527448,0.0001204257,0.0001794543,0.0008508043,0.0000886204,0.000306756,0.000005762693,0.00007379241,0.00001496314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003506986,"about_ca_system_score_gemma":0.00001182481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006748593,"about_ca_topic_score_gemma":0.0002585702,"domain_scores_codex":[0.9988017,0.00004359238,0.0002749306,0.0005147791,0.0001532886,0.0002116853],"domain_scores_gemma":[0.9989733,0.0002720925,0.00008501352,0.0004911216,0.00008135947,0.0000971843],"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":[0.000009075791,0.00007033351,0.00009510009,0.000005007164,0.0001097466,0.000001135509,0.00004036801,0.0877196,0.002307943,0.001021452,0.00001048431,0.9086097],"study_design_scores_gemma":[0.0001443123,0.0001612195,0.0005051646,0.00001970042,0.0002133486,0.000004906239,0.00001214895,0.6530217,0.3421591,0.003058383,0.0004099302,0.0002901251],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001331918,0.00005925807,0.9967243,0.001429255,0.00009827331,0.0001765784,0.00004159664,0.0001304259,0.000008379679],"genre_scores_gemma":[0.9916097,0.0003889227,0.007254904,0.0003111442,0.0000168363,0.0001302571,0.000004783627,0.00001121357,0.0002722347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9902778,"threshold_uncertainty_score":0.4910819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890100177105974,"score_gpt":0.2640982089617903,"score_spread":0.2451972071907306,"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."}}