Results 1  10
of
75,988
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract

Cited by 800 (26 self)
 Add to MetaCart
of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
Abstract

Cited by 529 (4 self)
 Add to MetaCart
the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks. Much progress has been made with feedforward networks, and attention
Statistical mechanics of complex networks
 Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
Abstract

Cited by 2083 (10 self)
 Add to MetaCart
Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
Abstract

Cited by 788 (23 self)
 Add to MetaCart
restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly
Parallel Networks that Learn to Pronounce English Text
 COMPLEX SYSTEMS
, 1987
"... This paper describes NETtalk, a class of massivelyparallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
Abstract

Cited by 548 (5 self)
 Add to MetaCart
is essential. (iv) Relearning after damage is much faster than learning during the original training. (v) Distributed or spaced practice is more effective for longterm retention than massed practice. Network models can be constructed that have the same performance and learning characteristics on a particular
Consensus and cooperation in networked multiagent systems
 PROCEEDINGS OF THE IEEE
"... This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview of ..."
Abstract

Cited by 772 (2 self)
 Add to MetaCart
This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
Abstract

Cited by 482 (8 self)
 Add to MetaCart
Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used
A theory of memory retrieval
 PSYCHOL. REV
, 1978
"... A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probememory item relatedness, just as a ringing tuning fork evokes sympath ..."
Abstract

Cited by 728 (81 self)
 Add to MetaCart
) and to speedaccuracy paradigms; results are found to provide a basis for comparison of these paradigms. It is noted that neural network models can be interfaced to the retrieval theory with little difficulty and that semantic memory models may benefit from such a retrieval scheme.
An evaluation of statistical approaches to text categorization
 Journal of Information Retrieval
, 1999
"... Abstract. This paper focuses on a comparative evaluation of a widerange of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine th ..."
Abstract

Cited by 664 (23 self)
 Add to MetaCart
were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performance; except for a Naive Bayes approach, the other learning algorithms also performed relatively well.
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
Abstract

Cited by 586 (13 self)
 Add to MetaCart
The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a
Results 1  10
of
75,988