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Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning


Apr 20, 2021
Zhenning Li, Hao Yu, Guohui Zhang, Shangjia Dong, Cheng-Zhong Xu

* Transportation Research Part C: Emerging Technologies Volume 125, April 2021, 103059 

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Mixup Without Hesitation


Jan 12, 2021
Hao Yu, Huanyu Wang, Jianxin Wu


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Robust Attacks on Deep Learning Face Recognition in the Physical World


Nov 27, 2020
Meng Shen, Hao Yu, Liehuang Zhu, Ke Xu, Qi Li, Xiaojiang Du


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S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation


Nov 04, 2020
Yuan Cheng, Yuchao Yang, Hai-Bao Chen, Ngai Wong, Hao Yu

* WACV2021 

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HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression


Feb 28, 2020
Rui Lin, Ching-Yun Ko, Zhuolun He, Cong Chen, Yuan Cheng, Hao Yu, Graziano Chesi, Ngai Wong

* 6 pages, 5 figures 

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On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization


May 10, 2019
Hao Yu, Rong Jin

* A short version is accepted to ICML 2019 

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On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization


May 09, 2019
Hao Yu, Rong Jin, Sen Yang

* A short version of this paper is accepted to ICML 2019 

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Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond


Nov 06, 2018
Krishna Kumar Singh, Hao Yu, Aron Sarmasi, Gautam Pradeep, Yong Jae Lee

* TPAMI submission. This is a journal extension of our ICCV 2017 paper arXiv:1704.04232 

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MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings


Nov 01, 2018
Hao Yu, Vivek Kulkarni, William Wang


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Parallel Restarted SGD for Non-Convex Optimization with Faster Convergence and Less Communication


Jul 17, 2018
Hao Yu, Sen Yang, Shenghuo Zhu


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DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization


Jun 07, 2018
Yuan Cheng, Guangya Li, Hai-Bao Chen, Sheldon X. -D. Tan, Hao Yu

* 10 pages, 9 figures and 2 tables 

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Fast K-Means Clustering with Anderson Acceleration


May 27, 2018
Juyong Zhang, Yuxin Yao, Yue Peng, Hao Yu, Bailin Deng


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Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors


Apr 10, 2018
Hao Yu, Zhaoning Zhang, Zheng Qin, Hao Wu, Dongsheng Li, Jun Zhao, Xicheng Lu

* 8 pages, 6 figures 

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Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications


Mar 24, 2018
Zheng Qin, Zhaoning Zhang, Shiqing Zhang, Hao Yu, Yuxing Peng

* 8 pages, 4 figures 

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S-OHEM: Stratified Online Hard Example Mining for Object Detection


Aug 15, 2017
Minne Li, Zhaoning Zhang, Hao Yu, Xinyuan Chen, Dongsheng Li

* 9 pages, 3 figures, accepted by CCCV 2017 

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Online Convex Optimization with Stochastic Constraints


Aug 12, 2017
Hao Yu, Michael J. Neely, Xiaohan Wei

* This paper extends our own ArXiv reports arXiv:1604.02218 (by considering more general stochastic functional constraints) and arXiv:1702.04783 (by relaxing a deterministic Slater-type assumption to a weaker stochastic Slater assumption; refining proofs; and providing high probability performance guarantees). See Introduction section (especially footnotes 1 and 2) for more details of distinctions 

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A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks


Jun 08, 2017
Yixing Li, Zichuan Liu, Kai Xu, Hao Yu, Fengbo Ren


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A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing


Dec 12, 2016
Zichuan Liu, Yixing Li, Fengbo Ren, Hao Yu


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A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and Finite Constraint Violations for Online Convex Optimization with Long Term Constraints


Oct 05, 2016
Hao Yu, Michael J. Neely

* In the previous version, both the regret bound and the constraint violation bound are $O(\sqrt{T})$. The current version improves the constraint violation bound from $O(\sqrt{T})$ to $O(1)$, i.e., a finite constant that is independent of T, while preserving the same $O(\sqrt{T})$ regret bound 

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