169 citations · 255 across the 16 of their papers we have counts for
16 papers
MoE-I: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
Cheng Yang, Yang Sui, Jinqi Xiao +7
The emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. Compared to traditional LLMs, MoE LLMs outperform traditional LLMs by…
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…
GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer
Miao Yin, Burak Uzkent, Yilin Shen +2
The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foun…
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…
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…
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition
Lizhi Xiang, Miao Yin, Chengming Zhang +4
Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tucker-compress…
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner
Xiao Zang, Miao Yin, Lingyi Huang +3
Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parall…
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…
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…
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Zihan Ding, Xiao-Yang Liu, Miao Yin +1
Deep generative models have been successfully applied to many applications. However, existing works experience limitations when generating large images (the literature usually gene…
Seismic facies recognition based on prestack data using deep convolutional autoencoder
Feng Qian, Miao Yin, Ming-Jun Su +2
Prestack seismic data carries much useful information that can help us find more complex atypical reservoirs. Therefore, we are increasingly inclined to use prestack seismic data f…