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LPNMR
2011
Springer
9 years 2 months ago
What Are the Necessity Rules in Defeasible Reasoning?
This paper investigates a new approach for computing the inference of defeasible logic. The algorithm proposed can substantially reduced the theory size increase due to transformat...
Ho-Pun Lam, Guido Governatori
LPNMR
2011
Springer
9 years 2 months ago
Logic, Probability and Computation: Foundations and Issues of Statistical Relational AI
Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) sta...
David Poole
WWW
2011
ACM
9 years 6 months ago
Web information extraction using Markov logic networks
In this paper, we consider the problem of extracting structured data from web pages taking into account both the content of individual attributes as well as the structure of pages...
Sandeepkumar Satpal, Sahely Bhadra, Sundararajan S...
JMLR
2010
163views more  JMLR 2010»
9 years 6 months ago
Dense Message Passing for Sparse Principal Component Analysis
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Kevin Sharp, Magnus Rattray
JMLR
2010
137views more  JMLR 2010»
9 years 6 months ago
HOP-MAP: Efficient Message Passing with High Order Potentials
There is a growing interest in building probabilistic models with high order potentials (HOPs), or interactions, among discrete variables. Message passing inference in such models...
Daniel Tarlow, Inmar Givoni, Richard S. Zemel
JMLR
2010
150views more  JMLR 2010»
9 years 6 months ago
Approximate parameter inference in a stochastic reaction-diffusion model
We present an approximate inference approach to parameter estimation in a spatio-temporal stochastic process of the reaction-diffusion type. The continuous space limit of an infer...
Andreas Ruttor, Manfred Opper
JMLR
2010
132views more  JMLR 2010»
9 years 6 months ago
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mu...
JMLR
2010
147views more  JMLR 2010»
9 years 6 months ago
Gaussian Processes for Machine Learning (GPML) Toolbox
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library ...
Carl Edward Rasmussen, Hannes Nickisch
JMLR
2010
141views more  JMLR 2010»
9 years 6 months ago
FastInf: An Efficient Approximate Inference Library
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
CORR
2011
Springer
199views Education» more  CORR 2011»
9 years 6 months ago
From Machine Learning to Machine Reasoning
A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition co...
Léon Bottou
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