{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129353"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129353","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Chavez, Jorge"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Zhang, Huan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-09","date_published":"2025-05-09","updated_at":"2026-07-22T22:25:05Z","subjects":["Machine Learning","Neural Network Verification"],"languages":["en","eng"],"rights":["Copyright 2025 Jorge Chavez"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129353","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Huan"]},{"key":"dc:creator","label":"Author","values":["Chavez, Jorge"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-09","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Neural Network Verification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Jorge Chavez"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129353"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Jorge Chavez, accepted the attached license on 2025-05-09 at 12:28.","The student, Jorge Chavez, submitted this Thesis for approval on 2025-05-09 at 12:35.","This Thesis was approved for publication on 2025-05-09 at 14:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22287 on 2025-10-19 at 18:14:04","The verification of neural network systems is crucial as the adoption of these systems are considered for safety-critical tasks. A neural network system that works empirically well may not be robust, and when employed in areas such as cyber-security and cyber-physical systems, the guaranteed performance is a must. Formal verification is a rapidly growing field that delves into providing these guarantees, ensuring that properties on these networks can be assured. This thesis serves as an introduction into the common techniques used to provide such guarantees. There is a particular focus on bound propagation techniques as such techniques have fueled state-of-the-art, efficient verifiers. After covering the many advances that have been made in neural network verification, we will delve further into the branch-and-bound paradigm that typically accompanies many existing verifiers, as well as demonstrate an insightful algorithm that is capable of garnering further efficacy from bound propagation verifiers."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Huan"],"dc:creator":["Chavez, Jorge"],"dc:date":["2025-05-09","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Jorge Chavez, accepted the attached license on 2025-05-09 at 12:28.","The student, Jorge Chavez, submitted this Thesis for approval on 2025-05-09 at 12:35.","This Thesis was approved for publication on 2025-05-09 at 14:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22287 on 2025-10-19 at 18:14:04","The verification of neural network systems is crucial as the adoption of these systems are considered for safety-critical tasks. A neural network system that works empirically well may not be robust, and when employed in areas such as cyber-security and cyber-physical systems, the guaranteed performance is a must. Formal verification is a rapidly growing field that delves into providing these guarantees, ensuring that properties on these networks can be assured. This thesis serves as an introduction into the common techniques used to provide such guarantees. There is a particular focus on bound propagation techniques as such techniques have fueled state-of-the-art, efficient verifiers. After covering the many advances that have been made in neural network verification, we will delve further into the branch-and-bound paradigm that typically accompanies many existing verifiers, as well as demonstrate an insightful algorithm that is capable of garnering further efficacy from bound propagation verifiers."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129353"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Jorge Chavez"],"dc:subject":["Machine Learning","Neural Network Verification"],"dc:title":["A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}