activity
20182023
most citedProgressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

26 citations · 58 across the 7 of their papers we have counts for

collaborators

14 papers

cs.CV2023

Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

Huiming Sun, Lan Fu, Jinlong Li +5

Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient…

eess.SP20231 cited

Loss Attitude Aware Energy Management for Signal Detection

Baocheng Geng, Chen Quan, Tianyun Zhang +2

This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approa…

cs.CL2020

Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning

Bingbing Li, Zhenglun Kong, Tianyun Zhang +4

Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and comput…

cs.LG20205 cited

Computation on Sparse Neural Networks: an Inspiration for Future Hardware

Fei Sun, Minghai Qin, Tianyun Zhang +3

Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are…

cs.LG20204 cited

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

Tianyun Zhang, Xiaolong Ma, Zheng Zhan +7

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categ…

cs.LG202011 cited

BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method

Xiaolong Ma, Zhengang Li, Yifan Gong +8

Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such…