Raymond K. W. Wong

We propose a new and identifiable parameterization of the computer model calibration problem and provide a general frequentist solution. Expand

This paper proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model which greatly improves direction estimation in regions with crossing fibers. Expand

We propose a full Bayesian approach to infer Boolean genetic networks from observed data. Expand

This paper proposes a new cepstrum estimation procedure that is capable of producing smoother and improved cephstrum estimates without the use of any parametric modeling. Expand

We present a modification of locally linear embedding with additive noise, which has been seen to perform better in the presence of noise distortion by exploiting the relationship between local linearity and reconstruction potential. Expand

This paper considers the problem of matrix completion when the observed entries are noisy and contain outliers. Expand

In this paper, we consider the double-sparsity model introduced by Rubinstein, Zibulevsky, and Elad where the dictionary itself is the product of a fixed, known basis and a data-adaptive sparse component. Expand

We provide a series of results for unsupervised learning with autoencoders. Expand