We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Because writing computer programs is hard, computer programmers are taught to use encapsulation and modularity to hide complexity and reduce the potential for errors. Their progra...
Anthony Cozzie, Frank Stratton, Hui Xue, Samuel T....
— This paper addresses the problem of vegetation detection from laser measurements. The ability to detect vegetation is important for robots operating outdoors, since it enables ...
Abstract—This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine lear...
Nguyen Thai-Nghe, Andre Busche, Lars Schmidt-Thiem...