PPT-Games and adversarial s
Author : pamella-moone | Published Date : 2018-11-04
earch Why study games Games are a traditional hallmark of intelligence Games are easy to formalize Games can be a good model of realworld competitive activities
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Games and adversarial s: Transcript
earch Why study games Games are a traditional hallmark of intelligence Games are easy to formalize Games can be a good model of realworld competitive activities Military confrontations negotiation auctions etc. 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. 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. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. 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. 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." . 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.. 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 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Attacks. Haotian Wang. Ph.D. . . Student. University of Idaho. Computer Science. Outline. Introduction. Defense . a. gainst . Adversarial Attack Methods. Gradient Masking/Obfuscation. Robust Optimization. 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.
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