PPT-ADVERSARIAL VS INQUISITORIAL

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. 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. 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) [. 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. Inquisitorial system . A system of trial where the court is in control in determining the facts and conduct of a trial. . Used in many European, Asian and South American countries.. role of the parties. —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 . 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." . 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. 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. 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. Attacks. Haotian Wang. Ph.D. . . Student. University of Idaho. Computer Science. Outline. Introduction. Defense . a. gainst . Adversarial Attack Methods. Gradient Masking/Obfuscation. Robust Optimization. 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.