{"id":"W3171847983","doi":"10.18653/v1/2021.naacl-main.85","title":"Posterior Differential Regularization with f-divergence for Improving Model Robustness","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Regularization (linguistics); Computational linguistics; Library science; Natural language processing; Computational biology; Artificial intelligence; Biology; Genetics; Gene","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":[0.00004905441,0.00008460573,0.00009176044,0.00002678458,0.0001004768,0.0001534891,0.0003071634,0.0000369739,0.00001121302],"category_scores_gemma":[0.00001233639,0.00007017568,0.00003020819,0.0001165157,0.00000929914,0.0003868757,0.0002066317,0.00003766437,8.416609e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002038833,"about_ca_system_score_gemma":0.0001226062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006352837,"about_ca_topic_score_gemma":0.0000133244,"domain_scores_codex":[0.9991628,0.00001261318,0.0001261615,0.0003674291,0.0001558999,0.0001751123],"domain_scores_gemma":[0.9993145,0.00001415173,0.00004253882,0.0003959232,0.0001878061,0.00004506489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002174804,0.00006330878,0.0002304395,0.0001006875,0.00001786231,0.00001139043,0.0004139653,0.7634493,0.0449485,0.109484,0.00003449193,0.08122428],"study_design_scores_gemma":[0.000253201,0.00001914862,0.00004990926,0.000008871993,0.000006568407,0.00001674879,0.00001322461,0.9825903,0.01617962,0.0007454717,0.00000364505,0.0001133352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08781508,0.000009922293,0.9113597,0.0003230065,0.0001636745,0.0001205187,0.000001284133,0.0001043048,0.0001025632],"genre_scores_gemma":[0.5218605,4.118346e-7,0.4762525,0.00006053745,0.00002823136,0.00001291739,0.000002985397,0.000005152907,0.001776779],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4351072,"threshold_uncertainty_score":0.2861681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01984097645139521,"score_gpt":0.2230929664217415,"score_spread":0.2032519899703463,"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."}}