Search
Results
-
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conventional algorithms for sparse signal recovery and sparse representation rely on l1-norm regularized variational methods. However, when applied to the reconstruction of sparse images, i.e., images where only a few pixels are non-zero, simple l1-norm-based methods ignore potential correlations in the support between adjacent pixels. In a number of applications, one is interested in images that are not only sparse, but also have a support ...Conference Paper -
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
(2016)Proceedings of Machine Learning Research ~ Proceedings of the International Conference on Machine Learning, 20-22 June 2016, New York, New York, USAStatistical models of student responses on assessment questions, such as those in homeworks and exams, enable educators and computer-based personalized learning systems to gain insights into students' knowledge using machine learning. Popular student-response models, including the Rasch model and item response theory models, represent the probability of a student answering a question correctly using an affine function of latent factors. ...Conference Paper