25 citations · 95 across the 14 of their papers we have counts for
5 papers · 1 filter
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
Jinqi Xiao, Chengming Zhang, Yu Gong +5
Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model comple…
Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
Siyu Liao, Chunhua Deng, Miao Yin +1
Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot ach…
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
Miao Yin, Siyu Liao, Xiao-Yang Liu +2
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model si…
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Xiao Zang, Yi Xie, Jie Chen +1
Deep neural networks, while generalize well, are known to be sensitive to small adversarial perturbations. This phenomenon poses severe security threat and calls for in-depth inves…
SGAD: Soft-Guided Adaptively-Dropped Neural Network
Zhisheng Wang, Fangxuan Sun, Jun Lin +2
Deep neural networks (DNNs) have been proven to have many redundancies. Hence, many efforts have been made to compress DNNs. However, the existing model compression methods treat a…