PPT-Adversarial Environments/

Game Playing Why Fun Many realworld applications in game theory Economics IndustryOperations Research Types of games well consider 2 player Zerosum one players loss is the others gain. 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. Cormac. Flanagan & Stephen Freund. UC Santa Cruz Williams . College. PLDI 2010. Slides by Michelle Goodstein. LBA Reading Group, June 2 2010. Motivation. Multi-threaded programs often contain data races. st. century. B R Sheerin. Senior Policy Analyst (Property). Discussion on the future of learning. Marc Prensky – Digital Natives, Digital Immigrants. David Thornburg – Campfires in Cyberspace: Primordial Metaphors for learning in the 21. Petri Nokelainen. School of Education. Old thinking on learning and environments. From ancient Greece compulsory education has been . subject centered. . . Learning, provided by professional teachers, has taken place in . Water environments. Most important factors. Salinity (how much salt). Depth. Cleanliness (pH, pollution, . etc. ). We will investigate. Freshwater. Seashores. Coral reefs. 2 types of fresh-water environments. 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. Mud Cracks in Rock. Current Environment. What is a Depositional Environment?. All rocks form in specific environments. An example of this that we talked about earlier are igneous rocks which are going to form in a volcanic area. So it you find a layer of igneous rock, you know that area once experienced volcanism. . —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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. (. 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 (. Presenter: Syed Sharjeelullah. Course: CS-732. Authors: Jefferson L. P. Lima. David Macedo. . Cleber. . Zanchettin. 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.