PPT-Adversarial Memory for Detecting Destructive Races

Cormac Flanagan amp Stephen Freund UC Santa Cruz Williams College PLDI 2010 Slides by Michelle Goodstein LBA Reading Group June 2 2010 Motivation Multithreaded programs often contain data races. 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. S. . Narayanasamy. , Z. Wang, J. . Tigani. , A. Edwards, B. Calder. UCSD and Microsoft. PLDI 2007. Data Races hard to debug. Difficult to detect. Even more difficult to reproduce. Data Race Detectors help in detection. OPEN FORUM. BRIAN KAVANAGH, VICE CHAIRMAN, IFHA. Quality Control of Group / Graded Races. Brian Kavanagh. Vice Chairman, IFHA. IFHA Basic Mission. “. The organisation of competitions to select the best horses in order to improve the quality of breeding”. 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. Constructed Response. Types. Constructed Response is the written response to a question. . The answer is gathered from a text, prompt, diagram, map, etc. . There are three types:. Constructed Response . 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. Constructive. vs. . Destructive. Processes. Constructive vs. Destructive. Constructive forces affect the earth's surface by building it up while forming new crust and landforms like mountains, islands, deltas, and sand dunes.. What are Destructive Forces?. A destructive force is a process that lowers or tears down the surface features of the Earth. . Examples of Destructive Forces:. Destructive forces can occur in many ways. The following are common examples of destructive forces:. Detecting Variation. In populations or when comparing closely related species, one major objective is to identify variation among the samples. AKA, one of the main goals in genomics is to identify what genomic features make individuals/populations/species different. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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.. Roi Shillo, Nick Hoernle, Kobi Gal. Creativity is…. Ubiquitous. [Schank & Cleary 95]. Fundamental . [Boden, 98]. Machine recognisable . [Newell, Shaw & Simon 62]. Focus for EDM. Open Ended Environments. 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.