ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search

Object Tracking via Pixel-Wise and Block-Wise Sparse Representation

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration stra...

A Block-Wise random sampling approach: Compressed sensing problem

The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...

Block-Wise Non-Malleable Codes

Non-malleable codes, introduced by Dziembowski, Pietrzak, and Wichs (ICS ’10) provide the guarantee that if a codeword c of a message m, is modified by a tampering function f to c′, then c′ either decodes to m or to “something unrelated” to m. It is known that non-malleable codes cannot exist for the class of all tampering functions and hence a lot of work has focused on explicitly constructing...

Point-wise Convolutional Neural Network

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network i...