{"id":"W4415549799","doi":"10.1007/978-3-032-08707-2_14","title":"Solution-Aware Vs Global ReLU Selection: Partial MILP Strikes Back for DNN Verification","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Agence Nationale de la Recherche","keywords":"Binary number; Set (abstract data type); Lipschitz continuity; Binary decision diagram; Upper and lower bounds; Branching (polymer chemistry); Branch and bound; Deep neural networks","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"],"consensus_categories":[],"category_scores_codex":[0.0008798229,0.0005826802,0.0005637314,0.0004371682,0.0006929399,0.0005873773,0.003103645,0.0005123384,0.00003486859],"category_scores_gemma":[0.0003405439,0.0006055248,0.0002080945,0.001112804,0.0004693547,0.0007621116,0.001030921,0.0008057223,0.00003979803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043374,"about_ca_system_score_gemma":0.00146375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004681832,"about_ca_topic_score_gemma":0.00009628299,"domain_scores_codex":[0.995633,0.00007626481,0.0006527237,0.001984303,0.0008675615,0.0007861528],"domain_scores_gemma":[0.9970935,0.0006529347,0.0004257918,0.001160805,0.0005150235,0.0001519255],"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":[0.00006397197,0.00004470397,0.0003323188,0.0001494374,0.00003667343,0.00001036091,0.0002613537,0.3982422,0.00006355253,0.09456934,0.001106483,0.5051196],"study_design_scores_gemma":[0.0004443334,0.0001940608,0.0001178597,0.0003058549,0.00001983389,0.00002391746,2.064308e-7,0.9360219,0.0004430253,0.04707897,0.01473992,0.0006100995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000008850888,0.0002000416,0.9899724,0.002835564,0.004160887,0.0007578628,0.00002087368,0.0002723603,0.001771209],"genre_scores_gemma":[0.05466251,0.00003094426,0.9406579,0.001474591,0.001634042,0.00005326995,0.00004595811,0.00004064133,0.001400123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5377797,"threshold_uncertainty_score":0.9996396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736942396144166,"score_gpt":0.2747101416153636,"score_spread":0.2573407176539219,"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."}}