Tsang, and L. Chia, "Sparse representation with kernels," To appear in IEEE Transactions on Image Processing., 2013.S. Gao, I. W.-H. Tsang, and L.-T. Chia, "Sparse representation with kernels," IEEE Trans. Image
Sparse Representation With Kernels 来自 国家科技图书文献中心 喜欢 0 阅读量: 7 作者:S Gao,WH Tsang,LT Chia 摘要: Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear ...
3.3 | Decorrelated Sparse Representation The decorrelated representations prevent co‐adaptation be- tween convolution kernels, but RdrðT Þ may update weights of convolution kernels with fixed pattern. It remains convolution kernels fail to learn features and have redundancy. Therefore, we apply ...
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’
This gives w0,∗ = arg min w 0 such that Xw = y, w (2) where X ∈ RN×D with N D whose columns are the elements of the different bases to be used in the representation, y the vector of signal values to be represented, and w the coefficients in the coordinate system defined ...
assumptions, concludes a graph of capturing the geometrical structure of data in a way that near data points from the same manifold connect with a higher weight than those from different manifolds. The spectral properties of the learned graph are exploited to portray a new representation of data....
SNICIT leverages data clustering to transform intermediate results into a sparser representation that largely reduces computation over inference iterations. Evaluated on both HPEC Graph Challenge benchmarks and conventional DNNs (MNIST, CIFAR-10), SNICIT achieves 6 ∼ 444× and 1.36 ∼ 1.95× ...
The host code required to create a JDS representation and to launch SpMV kernels on each section of the JDS representation is left as an exercise. Note that we want each section to have a large number of rows so that its kernel launch will be worthwhile. In the extreme cases where a ...
We present a novel dictionary learning (DL) approach for sparse representation based classification in kernel feature space. These sparse representations are obtained using dictionaries, which are learned using training exemplars that are mapped into a high-dimensional feature space using the kernel trick...
and at the end of the execution the results are combined in matrix C. The function allocates device memory for the CSR representation of the matrix A , as well as device memory for the part of matrix B and C on each device. The memcpys are done in separate streams for each device fo...