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The Next Generation of Neural Networks
Google Tech Talks
November, 29 2007
In the 1980's, new learning algorithms for neural networks promised to
solve difficult classification tasks, like speech or object recognition,
by learning many layers of non-linear features. The results were
disappointing for two reasons: There was never enough labeled data to
learn millions of complicated features and the learning was much too slow
in deep neural networks with many layers of features. These problems can
now be overcome by learning one layer of features at a time and by
changing the goal of learning. Instead of trying to predict the labels,
the learning algorithm tries to create a generative model that produces
data which looks just like the unlabeled training data. These new neural
networks outperform other machine learning methods when labeled data is
scarce but unlabeled data is plentiful. An application to very fast
document retrieval will be described.
Speaker: Geoffrey Hinton
Geoffrey Hinton received his BA in experimental psychology from Cambridge in
1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did
postdoctoral work at Sussex University and the University of California San
Diego and spent five years as a faculty member in the Computer Science
department at Carnegie-Mellon University. He then became a fellow of the
Canadian Institute for Advanced Research and moved to the Department of
Computer Science at the University of Toronto. He spent three years from 1998
until 2001 setting up the Gatsby Computational Neuroscience Unit at University
College London and then returned to the University of Toronto where he is a
University Professor. He holds a Canada Research Chair in Machine Learning. He
is the director of the program on "Neural Computation and Adaptive Perception"
which is funded by the Canadian Institute for Advanced Research.
Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada,
and the Association for the Advancement of Artificial Intelligence. He is an
honorary foreign member of the American Academy of Arts and Sciences, and a
former president of the Cognitive Science Society. He received an honorary
doctorate from the University of Edinburgh in 2001. He was awarded the first
David E. Rumelhart prize (2001), the IJCAI award for research excellence
(2005), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award
for contributions to information technology (1992).
A simple introduction to Geoffrey Hinton's research can be found in his
articles in Scientific American in September 1992 and October 1993. He
investigates ways of using neural networks for learning, memory, perception and
symbol processing and has over 200 publications in these areas. He was one of
the researchers who introduced the back-propagation algorithm that has been
widely used for practical applications. His other contributions to neural
network research include Boltzmann machines, distributed representations,
time-delay neural nets, mixtures of experts, Helmholtz machines and products of
experts. His current main interest is in unsupervised learning procedures
for neural networks with rich sensory input.
Length: 3563
Rating: 4.90 (190 ratings)
Tags: google techtalks techtalk engedu talk talks googletechtalks education
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Cracking Wireless Networks
The video shows to how crack WEP- or WPA-secured networks. It also shows how to prevent people from cracking your wireless network(s). ... (more)
Length: 514
Rating: 4.40 (501 ratings)
Tags: cracking wireless WEP WPA prevention
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Next Generation All-IP Telecom Networks: Quality of Service Challenges and Is...
Google Tech Talks
January, 14 2008
ABSTRACT
The SIP-based IP Multimedia Subsystem (IMS), while recently introduced, has become one of the primary distinguishing features of the next generation of mobile telecommunication systems. IMS allows mobile operators to offer advanced value-added services - like VoIP, so-called push-to-media, video, interactive gaming, and mobile banking - to their customers timely and efficiently. Google's plans to enter the wireless world open up a world of possibilities for offering customers and businesses advanced services such as targeted location-based services and advertisements through the IMS framework.
Deploying IMS, however, is a non-trivial task. The core challenge for the telecom industry has been and will be the integration of the current radio access network (RAN) and IP transport infrastructure with the IMS domain. Within standardization bodies, efforts are underway to address the issues for call setup and mobility signaling, while developing unified user profile management and Quality of Service (QoS) architectures. The real goal is a standardized, IMS-centric, end-to-end unified signaling architecture.
To this end, this presentation provides an overview of IMS and QoS signaling over integrated RAN and IMS domains. By using an exemplary family media service, aspects and specifics of the end-to-end QoS invocation, control and policy enforcement, including roaming scenarios, are demonstrated. Based on laboratory measurements performed at Sprint-Nextel aided with simulations, the Post Dial Delay (PDD) delay is evaluated and some practical recommendations for delay reduction are presented. The presentation will conclude with discussion of open issues and viable solutions. This presentation should be of interest to Googlers who work on mobile related projects and intend to have a big picture of next generation mobile systems such as application development, and service and system integration with wireless operators.
This presentation is based on the article S. Zaghloul, A. Jukan, W. Alanqar: "Extending QoS from Radio Access to all-IP Core in 3G Networks - An Operator's Perspective," IEEE Communications Magazine, Sept 2007.
Speaker: Said Zaghloul
Fulbright alumnus and former Telecommunication Design Engineer at Sprint-Nextel
Research Staff Member, PhD Candidate
Institute of Computer and Communication Network Engineering
Technical University Carolo-Wilhelmina of Braunschweig, Germany
Length: 3390
Rating: 4.50 (28 ratings)
Tags: google techtalks techtalk engedu talk talks googletechtalks education
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Social networks and trust : NetTrust
Google Tech Talks
February, 28 2008
ABSTRACT
NetTrust is a system that embeds social context in browsing by combining individual histories, social networks, and explicit ratings. NetTrust combines an implicit and explicit means of data collection. This trust based system uses shared browsing histories from a user's self-selected social networks to create both explicit and implicit data collection. NetTrust targets the human element of trust. It projects how a social network can signal meaningful trust information that can make an educative browsing experience. NetTrust allows an individual to select their own trusted sources of information and rate particular sites as trustworthy (or not). NetTrust allows an individual to select their own trusted authoritative sources of information from a market of ratings agencies and combine these ratings with the reputation information from their individual social network. This paper will present the Net Trust system; the dorm-based homophily tests with implications and the undergraduate-focused user testing.
Speaker: Professor L. Jean Camp
Professor L. Jean Camp is the author of Trust and Risk in Internet Commerce (MIT Press), Economics of Identity Theft (Springer) and the editor of Economics of Information Security (Kluwer Academic). She has authored over one hundred works, including seventy peer-reviewed works and eighteen book chapters. In addition to presentations at peer-reviewed venues, she has made scores of invited presentations on four continents. Her service has included the Board of Directors of Computer Professionals for Social Responsibility, the Board of Governors of the IEEE Society on Social Implications of Technology, Senior Member of the IEEE, and longstanding member of the USACM. See http://www.ljean.com/cv.html for more detailed information and full text of various publications.
Length: 3440
Rating: 4.80 (9 ratings)
Tags: google techtalks techtalk engedu talk talks googletechtalks education
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Lecture - 27 Learning : Neural Networks
Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, IIT Kharagpur. For more Courses visit http://nptel.iitm.ac.in
Length: 3595
Rating: 4.70 (25 ratings)
Tags: Learning Neural Networks
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