Kernel regressionModel-based optimizationSparsityVariable selectionThis article introduces the supervised deep learning method sparse kernel deep stacking networks (SKDSNs), which extend traditional kernel deep stacking networks (KDSNs) by incorporating a set of data-driven regularization and variable ...
总结:使用Roofline Model分析了SpMM Baseline 算术强度 Arithmetic Intensity 过低导致其无法发挥GPU性能,因此通过Shared-memory & Register Tiling技术,设计了一个Tiled SpMM核函数,提高了算术强度,同时对稀疏矩阵A进行了Warp级别上的Rename和Reorder,提高CSR数据的合并访存度。 1. Introduction 本文只讨论SpMM Sparse DN...
deepstruct.sparse.DeepCellDAN: complex module based on a directed acyclic network and custom cells on third-order structures. Suitable for large-scale neural architecture search deepstruct.recurrent.MaskedDeepRNN: multi-layered network with recurrent layers which can be maskedWhat...
SNNs may offer a potential solution due to their sparse binary communication scheme that reduces the resource usage in the network10,11,12,13,14,15; however, it has been so far impossible to train deep SNNs that perform at the exact same level as ANNs. Multiple methods have been proposed ...
The parameters include: the kernel size (height × width), the stride lengths (vertical, horizontal), the quantity of zero-padding (top, bottom, left, right) applied to the input, and the number of kernels. Pooling layers takes a single window sliding step-by-step over the input. ...
Sparse kernel learning for image annotation. In Proceedings of the ICMR 2014—ACM International Conference on Multimedia Retrieval 2014, Glasgow, UK, 1–4 April 2014; pp. 113–120. [Google Scholar] Zhang, S.; Huang, J.; Li, H.; Metaxas, D.N. Automatic image annotation and retrieval ...
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds Lasse Hansen, Jasper Diesel, Mattias P. Heinrich ECCV 2018 Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network Feng Mao, Xiang Wu, Hui Xue, Rong Zhang ECCV 2018 Graph R-CNN for Scene Graph...
In this paper, we present a new framework which incorporates a deep neural network that can be used to learn game strategies based on a kernel-based Monte Carlo tree search that finds actions within a continuous space. To avoid hand-crafted features, we train our network using supervised ...
A combination of support vector machines, sparse-coding methods, and hand-coded feature extractors with fully convolutional neural networks (FCNN) and deep residual networks into ensembles was evaluated. The experimental results emphasized that the integrated multitude of machine-learning methods achieved ...
The model consists of 16 convolutional layers with strides of 1 and kernel sizes of 3 × 3, where the feature depth gradually increases from 16 to 64 output channels (Fig. 1b). In between the convolutional layers, down-sampling is performed by three max pooling layers with a 2 ×...