Abstract. We define a novel, basic, unsupervised learning problem learning the the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task...
Abstract--We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its ef...
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin C...
The stability of low-rank matrix reconstruction is investigated in this paper. The -constrained minimal singular value ( -CMSV) of the measurement operator is shown to determine t...
Test campaigns usually require only a restricted subset of paths in a program to be thoroughly tested. As random testing (RT) offers interesting fault-detection capacities at low ...
The number of processors embedded in high performance computing platforms is growing daily to solve larger and more complex problems. The logical network topologies must also suppo...