research
∙
12/21/2021
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting
Deep neural networks (DNNs) have shown to provide superb performance in ...
share
research
∙
04/12/2020
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
To address the large model size and intensive computation requirement of...
research
∙
06/09/2019
Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense
The worst-case training principle that minimizes the maximal adversarial...
research
∙
03/23/2019
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
research
∙
10/17/2018
Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving human-level performance i...
research
∙
07/29/2018
ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs
Weight pruning methods of deep neural networks (DNNs) have been demonstr...
research
∙
04/10/2018
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Weight pruning methods for deep neural networks (DNNs) have been investi...
research
∙
02/15/2018
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
We present a systematic weight pruning framework of deep neural networks...
research
∙
10/18/2017
