661 citations · 2.1k across the 57 of their papers we have counts for
9 papers · 1 filter
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu +2
In recent years, deep neural networks demonstrated state-of-the-art performance in a large variety of tasks and therefore have been adopted in many applications. On the other hand,…
Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
The performance of Deep Neural Networks (DNNs) keeps elevating in recent years with increasing network depth and width. To enable DNNs on edge devices like mobile phones, researche…
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
Nathan Inkawhich, Matthew Inkawhich, Yiran Chen +1
The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video d…
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Hsin-Pai Cheng, Patrick Yu, Haojing Hu +4
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed…
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo +4
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted…
Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
Juncheng Shen, Juzheng Liu, Yiran Chen +1
Privacy recently emerges as a severe concern in deep learning, that is, sensitive data must be prohibited from being shared with the third party during deep neural network developm…
DPatch: An Adversarial Patch Attack on Object Detectors
Xin Liu, Huanrui Yang, Ziwei Liu +3
Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards ma…
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
Wei Wen, Yandan Wang, Feng Yan +4
In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a training method because of its efficiency; however, recently, a problem in SGD gains research interest:…
Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation
Jianlei Yang, Xueyan Wang, Qiang Zhou +5
Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips. Efficient obfuscation implem…