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JMLR
2012
9 years 10 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
UAI
1990
11 years 9 months ago
Ideal reformulation of belief networks
The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for ...
Jack S. Breese, Eric Horvitz
UAI
1996
11 years 9 months ago
Network Engineering for Complex Belief Networks
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a ...
Suzanne M. Mahoney, Kathryn B. Laskey
UAI
1996
11 years 9 months ago
Efficient Search-Based Inference for noisy-OR Belief Networks: TopEpsilon
Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been sh...
Kurt Huang, Max Henrion
UAI
1996
11 years 9 months ago
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, t...
Max Henrion, Malcolm Pradhan, Brendan Del Favero, ...
FLAIRS
1998
11 years 9 months ago
Decision Making in Qualitative Influence Diagrams
The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex...
Silja Renooij, Linda C. van der Gaag
UAI
2003
11 years 9 months ago
A Simple Insight into Iterative Belief Propagation's Success
In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitr...
Rina Dechter, Robert Mateescu
UAI
2004
11 years 9 months ago
Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with con...
Rina Dechter, Robert Mateescu
MMNS
2001
110views Multimedia» more  MMNS 2001»
11 years 9 months ago
A Framework for Supporting Intelligent Fault and Performance Management for Communication Networks
Abstract. In this paper, we present a framework for supporting intelligent fault and performance management for communication networks. Belief networks are taken as the basis for k...
Hongjun Li, John S. Baras
CL
2000
Springer
12 years 9 days ago
Logic, Knowledge Representation, and Bayesian Decision Theory
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...
David Poole
books