25 citations · 95 across the 14 of their papers we have counts for
15 papers
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…
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition
Yu Gong, Miao Yin, Lingyi Huang +3
Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size…
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Yang Sui, Miao Yin, Yi Xie +3
Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the imp…
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework
Miao Yin, Yang Sui, Siyu Liao +1
Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neu…
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure
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…
Noise Injection-based Regularization for Point Cloud Processing
Xiao Zang, Yi Xie, Siyu Liao +2
Noise injection-based regularization, such as Dropout, has been widely used in image domain to improve the performance of deep neural networks (DNNs). However, efficient regulariza…
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…
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems
Yi Xie, Cong Shi, Zhuohang Li +3
As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-re…
PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
Chunhua Deng, Siyu Liao, Yi Xie +3
Deep neural network (DNN) has emerged as the most important and popular artificial intelligent (AI) technique. The growth of model size poses a key energy efficiency challenge for…
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model
Yi Xie, Zhuohang Li, Cong Shi +3
Recently, the vulnerability of DNN-based audio systems to adversarial attacks has obtained the increasing attention. However, the existing audio adversarial attacks allow the adver…
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…
Embedding Compression with Isotropic Iterative Quantization
Siyu Liao, Jie Chen, Yanzhi Wang +2
Continuous representation of words is a standard component in deep learning-based NLP models. However, representing a large vocabulary requires significant memory, which can cause…
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator
Huy Phan, Yi Xie, Siyu Liao +2
Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many AI fields. Adversarial attack is a method that causes the intended misclas…
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…