PPT-Adversarial

Search game playing search We have experience in search where we assume that we are the only intelligent entity and we have explicit control over the world Let us consider what happens when we relax those assumptions. MEDIATING AND AVOIDING ADVERSARIAL VIOLENCE  By THOMAS DIGRAZIA Bio Tom D iGrazia was born in NYC. He received a JD from the University of Notre Dame Law School and MA in Political Science f Aram Harrow (UW -> MIT). Matt Hastings (Duke/MSR). Anup Rao (UW). The origins of determinism. Theorem [von Neumann]:. There exists a constant . p>0. such that for any circuit C there exists a circuit C’ such that. INQUISITORIAL. -Judge can ask the accused questions. -Accused must answer questions from lawyers as well as the judge. -Accused may not be presumed innocent and the burden of proof may be on them to prove their innocence. Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. 2012 . VP Debate. - SNL. Tina Fey/Sarah Palin. - SNL. Sarah and Hillary . - SNL. Clinton . pioneered the use of town meetings and television entertainment programs as a means of communicating directly with voters in the 1992 election. Andrea W. Richa. Arizona State University. SIROCCO'13, Andrea Richa. 1. Motivation. Channel availability hard to model:. Mobility. Packet injection. Temporary Obstacles. Background noise. Physical Interference. Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. ML Reading . Group. Xiao Lin. Jul. 22 2015. I. . Goodfellow. , J. . Pouget-Abadie. , M. Mirza, B. Xu, D. . Warde. -Farley, S. . Ozair. , A. . Courville. and Y. . Bengio. . . "Generative adversarial nets." . for . edge detection. Z. Zeng Y.K. Yu, K.H. Wong. In . IEEE iciev2018, International Conference on Informatics, Electronics & Vision '. June,kitakyushu. exhibition center, japan, 25~29, 2018. (. Deep Learning and Security Workshop 2017. Chang Liu. UC Berkeley. Deep Learning and Security is a trending topic in academia in 2017. Best Papers in Security Conferences. Towards Evaluating the Robustness of Neural Networks (Oakland 2017 Best Student Paper). DATAWorks. 2021 - . Test & Evaluation Methods for Emerging Technology and Domains. 04/16/21. Galen Mullins. Gautam . Vallabha. Aurora Schmidt. Sam Barham. Sean McDaniel. Eric . Naber. Tyler Young. Florian Tramèr. Stanford University, Google, ETHZ. ML suffers from . adversarial. . examples.. 2. 90% Tabby Cat. 100% Guacamole. Adversarial noise. Robust classification is . hard! . 3. Clean. Adversarial (. Dr. Alex Vakanski. Lecture 1. Introduction to Adversarial Machine Learning. . Lecture Outline. Machine Learning (ML). Adversarial ML (AML). Adversarial examples. Attack taxonomy. Common adversarial attacks. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity.