Chia, Sparse representation with kernels, IEEE Trans. Image Process. 22 (2) (2013) 423-434.GAO S H;TANG I W;CHIA L T.Sparse representation with kernels.Image Proeessing.2013.423-434Sparse Representation With Kernels. Shenghua Gao,Tsang, I.W.,Liang-Tien Chia. Image Processing, IEEE ...
Sparse Representation With Kernels 隐藏>> IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 2, FEBRUARY 2013 423 Sparse Representation With Kernels Shenghua Gao, Ivor Wai-Hung Tsang, and Liang-Tien Chia Abstract— Recent research has shown the initial success of sparse coding (Sc) in solving...
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....
Figure 1 shows the general matrix multiplication (GEMM) operation by using the block sparse format. On the left are the full matrix organized in blocks and its internal memory representation: compressed values and block indices. As the usual dense GEMM, the computation partitions the ou...
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...
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× ...
It is shown that, for random undersampling schemes, the new adaptive kernel is superior to traditional reduced interference distribution kernels. 展开 关键词: time-frequency analysis Kernel design reduced interference distribution sparse representation ...