PPT-Games and adversarial s

earch Why study games Games can be a good model of many competitive activities Military confrontations negotiation auctions Games are a traditional hallmark of intelligence Contrarian viewpoint textbook . 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. (Chapter 5). World Champion chess player Garry Kasparov . is . defeated by IBM’s Deep Blue chess-playing computer in a . six-game . match in May, . 1997. (. link. ). © Telegraph Group . Unlimited 1997. 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. Dr. Jey Veerasamy. jeyv@utdallas.edu. July 31. st. – August 23. rd. 9:30 am to 12 noon. 1. Memory game. 2. Minesweeper. 3. C. onnect4. 4. Deal or no deal. 5. Bouncing ball. 6. Sudoku. 7. —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 . earch. Why study games?. Games are a traditional hallmark of intelligence. Games are easy to formalize. Games can be a good model of real-world competitive activities. Military confrontations, negotiation, auctions, etc.. 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. (. Akrit Mohapatra. ECE Department, Virginia Tech. What are GANs?. System of . two neural networks competing against each other in a zero-sum game framework. . They were first introduced by . Ian Goodfellow. Use . adversarial learning . to suppress the effects of . domain variability. (e.g., environment, speaker, language, dialect variability) in acoustic modeling (AM).. Deficiency: domain classifier treats deep features uniformly without discrimination.. EXPERIMENTS”. Paper # 27. Vagan Terziyan,. Mariia Golovianko, Svitlana Gryshko & Tuure Tuunanen. ISM 2020. International Conference on Industry 4.0. and Smart Manufacturing. 25 November, 2020, . Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. 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.