Image-guided depth completion aims to generate dense maps from sparse guided by their corresponding color (RGB) images. In this paper, we propose deep using multi-affinity matrix. Recently, spatial propagation networks (SPNs) are used refine obtained initial completion. However, they use the same affinity matrix even in multiple iterations that has a limit improving performance, which is not ef...

The null space property and the restricted isometry property for a measurement matrix are two basic properties in compressive sampling, and are closely related to the sparse approximation. In this paper, we introduce the sparse approximation property of order s for a measurement matrix A: ‖xs‖2 ≤ D‖Ax‖2 + β σs(x) √ s for all x, where xs is the best s-sparse approximation of the vector x in `, σ...

The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand multiple-SpMxV frameworks for exploiting c...

We present our public-domain software for the following tasks in sparse (or toric) elimination theory, given a well-constrained polynomial system. First, C code for computing the mixed volume of the system. Second, Maple code for defining an overconstrained system and constructing a Sylvester-type matrix of its sparse resultant. Third, C code for a Sylvester-type matrix of the sparse resultant ...

Nonnegative matrix factorization is a powerful technique to realize dimension reduction and pattern recognition through single-layer data representation learning. Deep learning, however, with its carefully designed hierarchical structure, is able to combine hidden features to form more representative features for pattern recognition. In this paper, we proposed sparse deep nonnegative matrix fac...