PPT-Adversarial Examples, Generative Adversarial Networks, Deep

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 . approaches to alternations LING 451/551 Spring 2011 Generative view of phonology • Hayes 6.1.1 – ‘The morphology of  lnguge plces morphemes in dif composition . of . a breast . cancer . from multiple tissue samples. Habil Zare. Department of Genome Sciences. University of Washington. 19 Dec 2013. 1. Hypothesis. Because cancer is a heterogeneous disease, synergistic medications can treat it better than a single drug.. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. 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. 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. November 27 | . 2015. Facilitator. Mark Friesen. Consulting Manager, . Vantage Point. mfriesen@thevantagepoint.ca. @. markalanfriesen. Agenda. Introductions. Board Fundamentals | Organization Name. Governance. Li Deng . Deep Learning Technology Center. Microsoft AI and Research Group. Invited Presentation at NIPS Symposium, December 8, 2016. Outline. Topic one. : RNN versus Nonlinear Dynamic Systems;. sequential discriminative vs. generative models. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. An Overview. Yidong. Chai. 1,2. , . Weifeng Li. 1,3. , Hsinchun Chen. 1. 1 . Artificial Intelligence Laboratory, The University of Arizona. 2 . Tsinghua University. 3 . University of Georgia. 1. Acknowledgements. 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.. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. Deep Learning and Security Workshop 2017. Chang Liu. UC Berkeley. Deep Learning and Security is a trending topic in academia in 2017. Best Papers in Security Conferences. Towards Evaluating the Robustness of Neural Networks (Oakland 2017 Best Student Paper). Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. 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.