21 citations · 61 across the 14 of their papers we have counts for
9 papers · 1 filter
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Yang Sui, Miao Yin, Yu Gong +3
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature. O…
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Jinqi Xiao, Miao Yin, Yu Gong +3
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks. However, these emerging archite…
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…
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness
Huy Phan, Miao Yin, Yang Sui +2
Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustnes…
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…