activity
20182021
most citedPlug-and-Play Methods Provably Converge with Properly Trained Denoisers

101 citations · 118 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.LG20213 cited

A Design Space Study for LISTA and Beyond

Tianjian Meng, Xiaohan Chen, Yifan Jiang +1

In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling…

cs.LG20208 cited

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

Yang Zhao, Xiaohan Chen, Yue Wang +6

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…

cs.LG20205 cited

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Zepeng Huo, Arash PakBin, Xiaohan Chen +6

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unk…

cs.LG20191 cited

E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings

Yue Wang, Ziyu Jiang, Xiaohan Chen +4

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained pla…

cs.LG2018

Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

Xiaohan Chen, Jialin Liu, Zhangyang Wang +1

In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is st…