activity
20182021
most citedMEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

41 citations · 58 across the 6 of their papers we have counts for

collaborators

9 papers

cs.LG202141 cited

MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

Geng Yuan, Xiaolong Ma, Wei Niu +13

Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…

cs.LG2021

High-Robustness, Low-Transferability Fingerprinting of Neural Networks

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

This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-tr…

cs.SD202012 cited

RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

Peiyan Dong, Siyue Wang, Wei Niu +8

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniqu…

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.LG2019

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

Xiao Wang, Siyue Wang, Pin-Yu Chen +4

Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on…