Sciweavers

ACL
2012
12 years 1 months ago
Spectral Learning of Latent-Variable PCFGs
We introduce a spectral learning algorithm for latent-variable PCFGs (Petrov et al., 2006). Under a separability (singular value) condition, we prove that the method provides cons...
Shay B. Cohen, Karl Stratos, Michael Collins, Dean...
CCE
2006
13 years 11 months ago
Parameter estimation in continuous-time dynamic models using principal differential analysis
Principal differential analysis (PDA) is an alternative parameter estimation technique for differential equation models in which basis functions (e.g., B-splines) are fitted to dy...
A. A. Poyton, M. S. Varziri, K. B. McAuley, P. J. ...
ICML
2010
IEEE
14 years 7 days ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
WABI
2007
Springer
157views Bioinformatics» more  WABI 2007»
14 years 5 months ago
Composing Globally Consistent Pathway Parameter Estimates Through Belief Propagation
Abstract. Parameter estimation of large bio-pathway models is an important and difficult problem. To reduce the prohibitive computational cost, one approach is to decompose a large...
Geoffrey Koh, Lisa Tucker-Kellogg, David Hsu, P. S...
UM
2007
Springer
14 years 5 months ago
Identifiability: A Fundamental Problem of Student Modeling
In this paper we show how model identifiability is an issue for student modeling: observed student performance corresponds to an infinite family of possible model parameter estimat...
Joseph E. Beck, Kai-min Chang
RECOMB
2010
Springer
14 years 6 months ago
Incremental Signaling Pathway Modeling by Data Integration
Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental...
Geoffrey Koh, David Hsu, P. S. Thiagarajan
ICCV
2001
IEEE
15 years 1 months ago
What Value Covariance Information in Estimating Vision Parameters?
Many parameter estimation methods used in computer vision are able to utilise covariance information describing the uncertainty of data measurements. This paper considers the valu...
Michael J. Brooks, Wojciech Chojnacki, Darren Gawl...