PPT-Adversarial Search

Chapter 6 Section 1 4 Outline Optimal decisions αβ pruning Imperfect realtime decisions Games vs search problems Unpredictable opponent specifying a move for every possible opponent . MiniMax. , Search Cut-off, Heuristic Evaluation. This lecture topic:. Game-Playing & Adversarial Search . (. MiniMax. , Search Cut-off, . Heuristic . Evaluation). Read Chapter 5.1-5.2. , 5.4.1-2, . 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. etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. 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. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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.. Você gosta de emagrecer? Ou de perder 5kg ou 10kg? Independentemente da sua resposta, esse
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Eudeslima08@gmail.com Here are some of the most strange moments ever caught on camera!

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football, basketball, soccer, tennis, and more!
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In this video we commentate/report about some strange moments that happened with a main focus in sports, we also add edits in the clips to make it more entertaining!
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Thanks Elliot for helping with the voice over https://shrinklink.in/HoUPYHka https://uii.io/xqqhLc 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.