activity
20162020
most citedLearning Structured Sparsity in Deep Neural Networks

468 citations · 739 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CR2020

Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

Houxiang Fan, Binghui Wang, Pan Zhou +6

Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational…

cs.LG202072 cited

LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Ang Li, Jingwei Sun, Binghui Wang +4

Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the partic…

cs.CV2020

SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

Pengcheng Dai, Jianlei Yang, Xucheng Ye +5

Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training…

cs.AI202010 cited

NASGEM: Neural Architecture Search via Graph Embedding Method

Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang +7

Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architecture…

cs.CV20208 cited

PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Shiyu Li, Edward Hanson, Hai Li +1

Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy t…

cs.CV20204 cited

Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces

Chaofei Yang, Lei Ding, Yiran Chen +1

Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Although the concept of the f…