PPT-Computing with adversarial noise

Aram Harrow UW gt MIT Matt Hastings DukeMSR Anup Rao UW The origins of determinism Theorem von Neumann There exists a constant pgt0 such that for any circuit C there exists a circuit C such that. You can often stop noise that disturbs you without involving your council the police or the Environment Protection Authority EPA This brochure outlines steps you can take to prevent noise being an issue for you When noise annoys There are laws that If excessive noise is not reduced to a reasonable level straight away following the issue of an excessive noise direction the noise control officer accompanied by a Police officer may enter the premises and remove whatever is causing the noise or re Enacted 1988 Sections 1 to 3 8 to 12 18 to39 and 41 17 February 1989 N 44 of 1989 Sections 61 4 5 and 6and 40 in relation to items5bii and e 6 8 and 9 of the Schedule 17 August 1989 Section 63 17 November 1989 Section 4 5 131a and cand 2 to 8 Uni processor computing can be called centralized computing brPage 3br mainframe computer workstation network host network link terminal centralized computing distributed computing A distributed system is a collection of independent computers interc lnput at 1kHz 1mV RMS PreEmphasized FREQUENCY Hz 20 02000 DEVIATION dB 00 020000 040000 060000 100 1k 10k 50k 04000 06000 08000 1000 080000 10000 18V 25 25 MEASURED COMPUTER SIMULATED T1115 TA02 INPUT SELECT PER PHOTO CART RIDGE COM IN 475k MM 100 —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . 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.. 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. 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.