Machine Learning and the Market for Intelligence
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Retail for Lease 2987 Granville Street Vancouver, British Columbia
RETAIL FOR LEASE 2987 GRANVILLE STREET VANCOUVER, BRITISH COLUMBIA Exceptional Yaletown Retail Opportunity CONTACT US Mario Negris Nolan Toigo Personal Real Estate Corporation Sales Representative Executive Vice President +1 778 372 3938 +1 604 662 3000 [email protected] [email protected] URBAN | PROPERTIES | GROUP WEST 5TH AVENUE UNO LANGANN INE ART AVAILABLE TAL SHOP CHEESECAE ETC. IRA HAA SPA ATINSONS WEST 6TH AVENUE UNER CONSTRUCTION ASTERPIECE PAULS PLACE OLETTERY ART|ANTIUES|ESIGN URBAN ITNESS ARARAT RUGS SCOTT ART RAIN SONS ART AVYAN CARPETS CANAIAN CANNABIS CANCER RESERACH ASTERS GALLERY HEEL INE ART STARBUCS AUCTION GALLERY WEST 7TH AVENUE VACANT INUSTRIAL REVOLUTION ONA NELLIS IAN TAN GALLERY AVAILABLE CACHE COUTURE VANCOUVER HAIR ACAEY URBAN BARN OUGLAS REYNOLS GALLERY SUYA HOE ECOR ALISON BOUTIUE THE BRIC GRANVILLE STREET AVAILABLE WEST 8TH AVENUE STRUCTUBE PA PAYRY RUG VILLA BEAU INTERIORS VERANAH ANTIUES CALIORNIA CLOSETS URBANITY SG P GALLERY ALLURE NAILS SPA UBREAITII RESH SLICE LE SALON HAE SOE SHOP URBATO GALLERY RE RUBY HAIR STUIO RBC AVAILABLE WEST BROADWAY BLEN COEE CONALS RESTAURANT CHOW SANG INIGO 2 LEVELS EWELLERY E3 URNITURE BURRITO CURRENCY STARBUCS ECHANGE SUSHI VAN GRANVILLE GREEN RHINO EICAL CLINIC IREHALL CANNABIS VANCOUVER SHOE REPAIR PHARASAVE RESTORATION HARWARE PUBLIC LIBRARY EWAR CHAPAN WOAN W 10TH AVENUE BAN O ONTREAL POTTERY BARN AVAILABLE 2 LEVELS ASON . REE PEOPLE AS BEER WINE SPIRITS SOT OC THE ARUIS HOUSE O NIVES ASHIA OE ASHION ROOTS ECCO STARBUCS COEE EYES OR YOU LUSSO BABY WIRELESS -
AAAI 2008 Workshop Reports
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science 5-2009 AAAI 2008 Workshop Reports Mark Dredze University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Mark Dredze, "AAAI 2008 Workshop Reports", . May 2009. Sarabjot Singh Anand, Razvan Bunescu, Vitor Carvalho, Jan Chomicki, Vincent Conitzer, Michael T Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H Göker, Hans Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker. (2009, April). AAAI 2008 Workshop Reports. AI Magazine, 30(1), 108-118. Copyright AAAI 2009. The copies do not imply AAAI endorsement of a product or a service of the employer, and that the copies are not for sale. Publisher URL: http://proquest.umi.com/ pqdlink?did=1680350011&sid=1&Fmt=2&clientId=3748&RQT=309&VName=PQD This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/904 For more information, please contact [email protected]. AAAI 2008 Workshop Reports Abstract AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An vE olving Synergy. -
Objective Assessment of Cerebellar Ataxia: a Comprehensive and Refned Approach Bipasha Kashyap1 ✉ , Dung Phan1, Pubudu N
www.nature.com/scientificreports OPEN Objective Assessment of Cerebellar Ataxia: A Comprehensive and Refned Approach Bipasha Kashyap1 ✉ , Dung Phan1, Pubudu N. Pathirana1, Malcolm Horne2, Laura Power3 & David Szmulewicz2,3,4 Parametric analysis of Cerebellar Ataxia (CA) could be of immense value compared to its subjective clinical assessments. This study focuses on a comprehensive scheme for objective assessment of CA through the instrumented versions of 9 commonly used neurological tests in 5 domains- speech, upper limb, lower limb, gait and balance. Twenty-three individuals diagnosed with CA to varying degrees and eleven age-matched healthy controls were recruited. Wearable inertial sensors and Kinect camera were utilised for data acquisition. Binary and multilabel discrimination power and intra-domain relationships of the features extracted from the sensor measures and the clinical scores were compared using Graph Theory, Centrality Measures, Random Forest binary and multilabel classifcation approaches. An optimal subset of 13 most important Principal Component (PC) features were selected for CA- control classifcation. This classifcation model resulted in an impressive performance accuracy of 97% (F1 score = 95.2%) with Holmesian dimensions distributed as 47.7% Stability, 6.3% Timing, 38.75% Accuracy and 7.24% Rhythmicity. Another optimal subset of 11 PC features demonstrated an F1 score of 84.2% in mapping the total 27 PC across 5 domains during CA multilabel discrimination. In both cases, the balance (Romberg) test contributed the most (31.1% and 42% respectively), followed by the peripheral tests whereas gait (Walking) test contributed the least. These fndings paved the way for a better understanding of the feasibility of an instrumented system to assist informed clinical decision- making. -
The Founder of the Creative Destruction Lab Describes Its Moonshot Mission to Create a Canadian AI Ecosystem
The founder of the Creative Destruction Lab describes its moonshot mission to create a Canadian AI ecosystem. Thought Leader Interview: by Karen Christensen Describe what happens at the Creative Destruction Lab by companies that went through the Lab. When we finished our (CDL). fifth year in June 2017, we had exceeded $1.4 billion in equity The CDL is a seed-stage program for massively scalable science- value created. based companies. Some start-ups come from the University of Toronto community, but we now also receive applications from What exactly does the Lab provide to entrepreneurs? Europe, the U.S. (including Silicon Valley), Israel and Asia. Start-up founders benefit from a structured, objectives-oriented We launched the program in September 2012, and each process that increases their probability of success. The process is autumn since, we’ve admitted a new cohort of start-ups into orchestrated by the CDL team, while CDL Fellows and Associ- the program. Most companies that we admit have developed a ates generate the objectives. Objective-setting is a cornerstone working prototype or proof of concept. The most common type of the process. Every eight weeks the Fellows and Associates set of founder is a recently graduated PhD in Engineering or Com- three objectives for the start-ups to achieve, at the exclusion of puter Science who has spent several years working on a problem everything else. In other words, they define clear goals for an and has invented something at the frontier of their field. eight week ‘sprint’. Objectives can be business, technnology or The program does not guarantee financing, but the majority HR-oriented. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
A Strategic Metagame Player for General Chesslike Games
From: AAAI Technical Report FS-93-02. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved. A Strategic Metagame Player for General Chess-Like Games Barney Pell* Computer Laboratory University of Cambridge Cambridge, CB2 3QG, UK b dp((~cl c am. ac Abstract game will transfer also to new games. Such criteria in- clude the use of learning and planning, and the ability This paper reviews the concept of Metagameand to play more than one game. discusses the implelnentation of METAGAMER,a program which plays Metagame in the class of 1.1 Bias when using Existing Games symmetric chess-like games, which includes chess, However,even this generality-oriented research is sub- Chinese-chess, checkers, draughts, and Shogi. The ject to a potential methodological bias. As the human l)rogram takes as input the rules of a specific game researchers know at the time of program-development and analyses those rules to construct for that game which specific game or games the program will be tested an efficient representation and an evaluation func- on, it is possible that they import the results of their tion, both for use with a generic search engine. The own understanding of the game dh’ectly into their pro- strategic analysis performed by the program relates gn’am. In this case, it becomes difficult to determine a set of general knowledgesources to the details of whether the subsequent performance of the program the particular game. Amongother properties, this is due to the general theory it implements, or merely analysis determines the relative value of the differ- to the insightful observations of its developer about the eat pieces in a given game. -
Radford M. Neal Curriculum Vitae, 25 April 2021
Radford M. Neal Curriculum Vitae, 25 April 2021 Biographical Information Personal Data Date of Birth: 12 Sept 1956 Citizenship: Canadian Home address: University address: 21 Shaftesbury Avenue, #106 Department of Statistical Sciences Toronto, Ontario M4T 3B4 University of Toronto M5S 3G3 U of T web page: http://www.cs.utoronto.ca/∼radford/ Blog (not affiliated with U of T): https://radfordneal.wordpress.com E-mail addresses: [email protected], [email protected] Degrees Awarded BSc (Honours) in Computer Science, University of Calgary, 1977 MSc in Computer Science, University of Calgary, 1980 Thesis title: An Editor for Trees Supervisor: David Hill Ph.D in Computer Science, University of Toronto, 1995 Thesis title: Bayesian Learning for Neural Networks Supervisor: Geoffrey Hinton Employment and Other Relevant Experience Present position: Professor Emeritus, Department of Statistical Sciences and Department of Computer Science, University of Toronto, Appointed as Lecturer 1 July 1994. Appointed as Associate Member (Limited Term) of the graduate faculty 13 January 1995. Promoted to Assistant Professor 1 July 1995. Promoted to Associate Professor and full member of the graduate faculty 1 July 1999. Promoted fo Full Professor 1 July 2001. Awarded a Canada Research Chair in Statistics and Machine Learning 1 July 2003 (held to 31 December 2016). Cross-appointed to the Dalla Lana School of Public Health 14 February 2006. Retired as of 1 January 2017. Earlier teaching experience: Sessional instructor in computer science at the University of Calgary for the following courses: 1 Summers 1979-1981 Taught half of the core second-year course on machine archi- tecture and programming. -
Abstract Counterexample-Based Refinement for Powerset
Abstract Counterexample-based Refinement for Powerset Domains R. Manevich1,†, J. Field2 , T. A. Henzinger3,§, G. Ramalingam4,¶, and M. Sagiv1 1 Tel Aviv University, {rumster,msagiv}@tau.ac.il 2 IBM T.J. Watson Research Center, [email protected] 3 EPFL, [email protected] 4 Microsoft Research India, [email protected] Abstract. Counterexample-guided abstraction refinement (CEGAR) is a powerful technique to scale automatic program analysis techniques to large programs. However, so far it has been used primarily for model checking in the context of predicate abstraction. We formalize CEGAR for general powerset domains. If a spurious abstract counterexample needs to be removed through abstraction refinement, there are often several choices, such as which program location(s) to refine, which ab- stract domain(s) to use at different locations, and which abstract values to compute. We define several plausible preference orderings on abstrac- tion refinements, such as refining as “late” as possible and as “coarse” as possible. We present generic algorithms for finding refinements that are optimal with respect to the different preference orderings. We also compare the different orderings with respect to desirable properties, in- cluding the property if locally optimal refinements compose to a global optimum. Finally, we point out some difficulties with CEGAR for non- powerset domains. 1 Introduction The CEGAR (Counterexample-guided Abstraction Refinement) paradigm [1, 3] has been the subject of a significant body of work in the automatic verification community. The basic idea is as follows. First, we statically analyze a program using a given abstraction. When an error is discovered, the analyzer generates an abstract counterexample, and checks whether the error occurs in the corre- sponding concrete execution path. -
A/B Testing Challenges in Large Scale Social Networks
From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks Ya Xu, Nanyu Chen, Adrian Fernandez, Omar Sinno, Anmol Bhasin LinkedIn Corp, 2029 Stierlin Court, Mountain View, CA 94043 {yaxu, nchen, afernandez, osinno, abhasin}@linkedin.com ABSTRACT experiments. The theory of controlled experiment dates back to Sir Ronald A. A/B testing, also known as bucket testing, split testing, or Fisher’s experiments at the Rothamsted Agricultural Experimental controlled experiment, is a standard way to evaluate user Station in England in the 1920s [11]. Since then, many textbooks engagement or satisfaction from a new service, feature, or and papers from different fields have provided theoretical product. It is widely used among online websites, including social foundations [20, 21, 32, 33] for running controlled experiments. network sites such as Facebook, LinkedIn, and Twitter to make While the theory may be straightforward, the deployment and data-driven decisions. At LinkedIn, we have seen tremendous mining of experiments in practice and at scale can be complex and growth of controlled experiments over time, with now over 400 challenging [13, 14]. In particular, several past KDD papers have concurrent experiments running per day. General A/B testing discussed at length the experimentation systems used at Microsoft frameworks and methodologies, including challenges and pitfalls, Bing and Google [8, 9], including best practices and pitfalls [7, have been discussed extensively in several previous KDD work 10]. Facebook also introduces the PlanOut language which [7, 8, 9, 10]. In this paper, we describe in depth the provides a toolkit for parameter-based experiments [12]. -
Microhoo: Lessons from a Takeover Attempt Nihat Aktas, Eric De Bodt
MicroHoo: Lessons from a takeover attempt Nihat Aktas, Eric de Bodt, and Richard Roll This draft: July 14, 2010 ABSTRACT On February 1, 2008, Microsoft offered $43.7 billion for Yahoo. This was a milestone in the Microsoft versus Google battle to control the internet search industry and the related online advertising market. We provide an in-depth analysis of this failed takeover attempt. We attempt to identify the sources of overbidding (signaling, hubris-overconfidence, rational overpayment) and we show that the corporate control market has a disciplinary dimension but of an incidental and latent nature. Our results highlight the existence of takeover anticipation premiums in the stock prices of the potential targets. JEL classification: G34 Keywords: Overbidding; Competitive advantage; Rational overpayment; Latent competition Aktas de Bodt Roll Univ. Lille Nord de EMLYON Business UCLA Anderson France School 110 Westwood Plaza ECCCS Address 23 av. Guy de Collongue Los Angeles, CA 90095- 1 place Déliot - BP381 69130 Ecully 1481 59020 Lille Cédex France USA France Voice +33-4-7833-7847 +33-3-2090-7477 +1-310-825-6118 Fax +33-4-7833-7928 +33-3-2090-7629 +1-310-206-8404 E-mail [email protected] eric.debodt@univ- [email protected] lille2.fr 1 MicroHoo: Lessons from a takeover attempt 1. Introduction Fast Internet access, software as a service, cloud computing, netbooks, mobile platforms, one-line application stores,…, the first decade of the twenty-first century brought all the ingredients of a new technological revolution. These new technologies share one common denominator: the Internet. Two large competitors are face to face. -
Lukas Biewald
Lukas Biewald 823 Capp St., San Francisco, CA 94110 (650) 804 5470 lukas@crowdflower.com Awards Netexplorateur of the Year http://www.netexplorateur.org/ (2010) TechCrunch 50 http://techcrunch50.com (2009) First Place, Caltech Turing Tournament http://turing.ssel.caltech.edu Work Founder, CEO: CrowdFlower 2008 — Present • Built leading Labor-on-Demand company from the ground up. Designed and coded core technology. Built customer base up from nothing to 100 customers including several Fortune-500 companies. Hired 25 employees. • Raised two rounds of financing totalling $6.2 million in 2009 in tough economic climate from well-known angels and VCs including Trinity, Bessemer, Founders´ Fund, Auren Hoffman, Aydin Senkut, Manu Kumar, Gary Kremen and Travis Kalanick. Senior Scientist: Powerset 2007 — 2008 • As the 20th employee, created and led two core teams: metrics and search ranking. • Developed the Amazon Mechanical Turk interface, identified as a key technology in Microsoft’s $100 million acquisition. Relevance Engineer, Engineering Manager: Yahoo! Inc. 2005 — 2007 • Technical lead for research, development and deployment of new web search ranking functions in five major markets. • Chosen as most productive employee in Applied Science business unit year end review. Research Assistant: Stanford AI Lab (sail.stanford.edu) 2003 — 2004 • Researched word-sense disambiguation, machine translation and Japanese word segmentation. • Published two papers with over 100 citations. Education Stanford University 1998 — 2004 BS in Mathematical and Computational Science with Honors (2003) MS in Computer Science with Concentration in Artificial Intelligence (2004) Publications David Vickrey, Lukas Biewald, Marc Teyssier, and Daphne Koller Word-Sense Disambiguation for Machine Translation. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2005. -
An Empirical Study on Evaluation Metrics of Generativeadversarialnetworks
Under review as a conference paper at ICLR 2018 AN EMPIRICAL STUDY ON EVALUATION METRICS OF GENERATIVE ADVERSARIAL NETWORKS Anonymous authors Paper under double-blind review ABSTRACT Despite the widespread interest in generative adversarial networks (GANs), few works have studied the metrics that quantitatively evaluate GANs’ performance. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the important problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting. Then with a series of carefully designed experiments, we are able to comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings. Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest-Neighbour (1-NN) two-sample test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space. Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far these state-of-the-art GANs are from perfect. 1 INTRODUCTION Generative adversarial networks (GANs) (Goodfellow et al., 2014) have been studied extensively in recent years. Besides producing surprisingly plausible images of faces (Radford et al., 2015; Larsen et al., 2015) and bedrooms (Radford et al., 2015; Arjovsky et al., 2017; Gulrajani et al., 2017), they have also been innovatively applied in, for example, semi-supervised learning (Odena, 2016; Makhzani et al., 2015), image-to-image translation (Isola et al., 2016; Zhu et al., 2017), and simulated image refinement (Shrivastava et al., 2016).