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...
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. ...
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...
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...
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...
Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental...
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...