activity
20172022
most citedPatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

211 citations · 475 across the 13 of their papers we have counts for

collaborators
Showing cs.LGShow all

14 papers · 1 filter

cs.LG2020

Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

Wei Niu, Pu Zhao, Zheng Zhan +3

High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage re…

cs.LG20201 cited

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

Kaidi Xu, Sijia Liu, Pin-Yu Chen +4

Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making poten…

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…

cs.LG20202 cited

AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks

Xiao Wang, Siyue Wang, Pin-Yu Chen +2

Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware…

cs.LG20202 cited

Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent

Pu Zhao, Pin-Yu Chen, Siyue Wang +1

Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains…

cs.LG2020

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

Kaidi Xu, Zhouxing Shi, Huan Zhang +6

Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has…