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Multiscalar Processors

by Gurindar S. Sohi, Scott E. Breach, T. N. Vijaykumar - In Proceedings of the 22nd Annual International Symposium on Computer Architecture , 1995
"... Multiscalar processors use a new, aggressive implementation paradigm for extracting large quantities of instruction level parallelism from ordinary high level language programs. A single program is divided into a collection of tasks by a combination of software and hardware. The tasks are distribute ..."
Abstract - Cited by 589 (30 self) - Add to MetaCart
Multiscalar processors use a new, aggressive implementation paradigm for extracting large quantities of instruction level parallelism from ordinary high level language programs. A single program is divided into a collection of tasks by a combination of software and hardware. The tasks

Gradient-based learning applied to document recognition

by Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner - Proceedings of the IEEE , 1998
"... Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract - Cited by 1533 (84 self) - Add to MetaCart
to deal with the variability of two dimensional (2-D) shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called graph

Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

by Wolfgang Maass, Thomas Natschläger, Henry Markram
"... A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model for real-time computing on time-var ..."
Abstract - Cited by 469 (38 self) - Add to MetaCart
-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead it is based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can

Open information extraction from the web

by Michele Banko, Michael J Cafarella, Stephen Soderland, Matt Broadhead, Oren Etzioni - IN IJCAI , 2007
"... Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to ma ..."
Abstract - Cited by 373 (39 self) - Add to MetaCart
and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set

A break in the clouds: Towards a cloud definition

by Luis M. Vaquero, Luis Rodero-merino, Juan Caceres, Maik Lindner, Telefonica Investigacion Y Desarrollo - ACM SIGCOMM Computer Communication Review , 2009
"... This article is an editorial note submitted to CCR. It has NOT been peer reviewed. The author takes full responsibility for this article’s technical content. Comments can be posted through CCR Online. This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Clou ..."
Abstract - Cited by 409 (5 self) - Add to MetaCart
Cloud is, using the main characteristics typically associated with this paradigm in the literature. More than 20 definitions have been studied allowing for the extraction of a consensus definition as well as a minimum definition containing the essential characteristics. This paper pays much attention

The Tradeoffs Between Open and Traditional Relation Extraction

by Michele Banko, Oren Etzioni
"... Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relationindependent extraction paradigm that is tailored to massive and heterogeneous corpora such as the Web. An Open IE system extracts a diverse set of relational tuples ..."
Abstract - Cited by 112 (10 self) - Add to MetaCart
Traditional Information Extraction (IE) takes a relation name and hand-tagged examples of that relation as input. Open IE is a relationindependent extraction paradigm that is tailored to massive and heterogeneous corpora such as the Web. An Open IE system extracts a diverse set of relational tuples

Mapping the Gnutella network: Properties of large-scale peer-to-peer systems and implications for system design

by Matei Ripeanu, Ian Foster, Adriana Iamnitchi - IEEE Internet Computing Journal , 2002
"... Despite recent excitement generated by the peer-to-peer (P2P) paradigm and the surprisingly rapid deployment of some P2P applications, there are few quantitative evaluations of P2P systems behavior. The open architecture, achieved scale, and self-organizing structure of the Gnutella network make it ..."
Abstract - Cited by 361 (23 self) - Add to MetaCart
Despite recent excitement generated by the peer-to-peer (P2P) paradigm and the surprisingly rapid deployment of some P2P applications, there are few quantitative evaluations of P2P systems behavior. The open architecture, achieved scale, and self-organizing structure of the Gnutella network make

Interactive Multi-Resolution Modeling on Arbitrary Meshes

by Leif Kobbelt , Swen Campagna, Jens Vorsatz, Hans-Peter Seidel , 1998
"... During the last years the concept of multi-resolution modeling has gained special attention in many fields of computer graphics and geometric modeling. In this paper we generalize powerful multiresolution techniques to arbitrary triangle meshes without requiring subdivision connectivity. Our major o ..."
Abstract - Cited by 307 (34 self) - Add to MetaCart
coefficients already provides effective and efficient algorithms to extract multi-resolution information from unstructured meshes. In combination with discrete fairing techniques, i.e., the constrained minimization of discrete energy functionals, we obtain very fast mesh smoothing algorithms which are able

Distant supervision for relation extraction without labeled data

by Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky
"... Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora ..."
Abstract - Cited by 239 (3 self) - Add to MetaCart
Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora

Image Segmentation by Data Driven Markov Chain Monte Carlo

by Zhuowen Tu, Song-Chun Zhu, Heung-yeung Shum , 2001
"... This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to exp ..."
Abstract - Cited by 277 (32 self) - Add to MetaCart
This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics
Results 1 - 10 of 1,833