PPT-Adversarial Models for Wireless Communication

Author : test | Published Date : 2018-02-28

Andrea W Richa Arizona State University SIROCCO13 Andrea Richa 1 Motivation Channel availability hard to model Mobility Packet injection Temporary Obstacles Background

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Adversarial Models for Wireless Communication: Transcript

Andrea W Richa Arizona State University SIROCCO13 Andrea Richa 1 Motivation Channel availability hard to model Mobility Packet injection Temporary Obstacles Background noise Physical Interference. 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. by Mike Denko, Alex Motalleb, and Tony Qian . Image taken from: http://findicons.com/icon/84804/wifi. Purpose. Want to make an embedded application where a wired connection isn't practical, things to consider with a wireless solution:. January 2013. Avaya IP Wireless Telephones. Campus Nomad. The “Campus Nomad” is mobile within the boundaries of the enterprise. This is most common in healthcare, retail and manufacturing environments.. 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) [. 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.. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. 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 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Generative Adversarial Networks (GANs). Generative Adversarial Networks (GANs). Goodfellow. et al (2014) . https://arxiv.org/abs/1406.2661. Minimize distance between the distributions of real data and generated samples. Presenter: Syed Sharjeelullah. Course: CS-732. Authors: Jefferson L. P. Lima. David Macedo. . Cleber. . Zanchettin. BY. Dr.K.Gopi. Associate professor. Department of ECE. SITAMS. Unit-1 Introduction to wireless networks. Introduction. Difference between wireless and fixed telephone networks. Development of wireless networks,.

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