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Results 1 - 10
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6,584
Loopy belief propagation for approximate inference: An empirical study. In:
- Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract
-
Cited by 676 (15 self)
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Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon
Factor Graphs and the Sum-Product Algorithm
- IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
Abstract
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Cited by 1791 (69 self)
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be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform algorithms.
A distributed, developmental model of word recognition and naming
- PSYCHOLOGICAL REVIEW
, 1989
"... A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagatio ..."
Abstract
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Cited by 706 (49 self)
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A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagation
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction
, 2003
"... Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for ..."
Abstract
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Cited by 406 (20 self)
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Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application
Policy gradient methods for reinforcement learning with function approximation.
- In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract
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Cited by 439 (20 self)
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policy. Large applications of reinforcement learning (RL) require the use of generalizing function approximators such neural networks, decision-trees, or instance-based methods. The dominant approach for the last decade has been the value-function approach, in which all function approximation effort goes
Computational Models of Sensorimotor Integration
- SCIENCE
, 1997
"... The sensorimotor integration system can be viewed as an observer attempting to estimate its own state and the state of the environment by integrating multiple sources of information. We describe a computational framework capturing this notion, and some specific models of integration and adaptati ..."
Abstract
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Cited by 424 (12 self)
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information from visual and auditory systems is integrated so as to reduce the variance in localization. (2) The effects of a remapping in the relation between visual and auditory space can be predicted from a simple learning rule. (3) The temporal propagation of errors in estimating the hand
Programming Context-Aware Pervasive Computing Applications with TOTA
, 2002
"... Pervasive computing calls for suitable programming models and associated supporting infrastructures to deal with large software systems dived in complex and dynamic network environments. Here we present TOTA, a new approach for the development of pervasive computing applications. TOTA proposes ..."
Abstract
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Cited by 4 (2 self)
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relying on tuple-based information to be spatially diffused in the network on the basis of some application-specific propagation rule, to be exploited by application agents to achieve context-awareness and to effectively coordinate with each other. As shown with the help of a case study scenario
Self-organisation in a perceptual network
- IEEE Computer
, 1988
"... young animal or child perceives and identifies features in its envi-, roument in an apparently effortless way. No presently known algorithms even approach this flexible, generalpurpose perceptual capability. Discovering the principles that may underlie perceptual processing is important both for neu ..."
Abstract
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Cited by 364 (0 self)
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; and (3) that can lead to profitable experimental programs, testable predictions, and applications to synthetic perception as well as neuroscientific understanding? I believe the answer is yes, and that the use of theoretical neural networks that embody biologically-motivated rules and constraints is a
Correctness of belief propagation in Gaussian graphical models of arbitrary topology
- NEURAL COMPUTATION
, 1999
"... Local "belief propagation" rules of the sort proposed byPearl [12] are guaranteed to converge to the correct posterior probabilities in singly connected graphical models. Recently, a number of researchers have empirically demonstrated good performance of "loopy belief propagation&q ..."
Abstract
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Cited by 296 (7 self)
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Local "belief propagation" rules of the sort proposed byPearl [12] are guaranteed to converge to the correct posterior probabilities in singly connected graphical models. Recently, a number of researchers have empirically demonstrated good performance of "loopy belief propagation
Handwritten Digit Recognition with a Back-Propagation Network
- Advances in Neural Information Processing Systems
, 1990
"... We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated d ..."
Abstract
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Cited by 285 (21 self)
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We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated
Results 1 - 10
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6,584
