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no code implementations • 14 Jan 2022 • Chen Wang, Zhongcai Pei, Shuang Qiu, Zhiyong Tang

Staircases are some of the most common building structures in urban environments.

no code implementations • 27 Nov 2021 • Weizhong Zhang, Shuang Qiu

To the best of our knowledge, this is the first screening method which introduces the dual optimum estimation technique -- by carefully exploring and exploiting the strong convexity and the complex structure of the dual problem -- in static screening methods to dynamic screening.

no code implementations • 19 Oct 2021 • Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang

Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase.

1 code implementation • 19 Jul 2021 • Dawei Du, Longyin Wen, Pengfei Zhu, Heng Fan, QinGhua Hu, Haibin Ling, Mubarak Shah, Junwen Pan, Ali Al-Ali, Amr Mohamed, Bakour Imene, Bin Dong, Binyu Zhang, Bouchali Hadia Nesma, Chenfeng Xu, Chenzhen Duan, Ciro Castiello, Corrado Mencar, Dingkang Liang, Florian Krüger, Gennaro Vessio, Giovanna Castellano, Jieru Wang, Junyu Gao, Khalid Abualsaud, Laihui Ding, Lei Zhao, Marco Cianciotta, Muhammad Saqib, Noor Almaadeed, Omar Elharrouss, Pei Lyu, Qi Wang, Shidong Liu, Shuang Qiu, Siyang Pan, Somaya Al-Maadeed, Sultan Daud Khan, Tamer Khattab, Tao Han, Thomas Golda, Wei Xu, Xiang Bai, Xiaoqing Xu, Xuelong Li, Yanyun Zhao, Ye Tian, Yingnan Lin, Yongchao Xu, Yuehan Yao, Zhenyu Xu, Zhijian Zhao, Zhipeng Luo, Zhiwei Wei, Zhiyuan Zhao

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.

3 code implementations • CVPR 2021 • Zhengxia Zou, Tianyang Shi, Shuang Qiu, Yi Yuan, Zhenwei Shi

Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering.

no code implementations • 23 Aug 2020 • Shuang Qiu, Zhuoran Yang, Xiaohan Wei, Jieping Ye, Zhaoran Wang

Existing approaches for this problem are based on two-timescale or double-loop stochastic gradient algorithms, which may also require sampling large-batch data.

no code implementations • ACL 2020 • Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.

no code implementations • 22 Jun 2020 • Shuang Qiu, Xiaohan Wei

The best known bound for solving this problem is $\mathcal{O}(\sqrt{T})$ regret and $\mathcal{O}(1)$ constraint violation, whose algorithms and analysis are restricted to Euclidean spaces.

1 code implementation • 12 May 2020 • Yu Wang, Rong Ge, Shuang Qiu

Unlike existing work in deep neural network (DNN) graphs optimization for inference performance, we explore DNN graph optimization for energy awareness and savings for power- and resource-constrained machine learning devices.

no code implementations • IEEE 2020 • Shuang Qiu, Yao Zhao, Jianbo Jiao, Yunchao Wei, Shikui Wei

To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem.

no code implementations • NeurIPS 2020 • Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang

In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode.

1 code implementation • 11 Oct 2019 • Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, Ji Liu

However, in many social network scenarios, centralized federated learning is not applicable (e. g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable).

no code implementations • 25 Sep 2019 • Shupeng Gui, Xiangliang Zhang, Pan Zhong, Shuang Qiu, Mingrui Wu, Jieping Ye, Zhengdao Wang, Ji Liu

The key problem in graph node embedding lies in how to define the dependence to neighbors.

no code implementations • NeurIPS Workshop Deep_Invers 2019 • Shuang Qiu, Xiaohan Wei, Zhuoran Yang

In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$.

no code implementations • ICML 2020 • Shuang Qiu, Xiaohan Wei, Zhuoran Yang

Specifically, we consider a new framework for this problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0 \in \mathbb{R}^k$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$ such that $\theta_0 = G(x_0)$.

no code implementations • 17 Jul 2019 • Hanlin Tang, Xiangru Lian, Shuang Qiu, Lei Yuan, Ce Zhang, Tong Zhang, Ji Liu

Since the \emph{decentralized} training has been witnessed to be superior to the traditional \emph{centralized} training in the communication restricted scenario, therefore a natural question to ask is "how to apply the error-compensated technology to the decentralized learning to further reduce the communication cost."

no code implementations • 30 Jan 2019 • Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin

This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.

no code implementations • 29 Jan 2019 • Yawei Zhao, Chen Yu, Peilin Zhao, Hanlin Tang, Shuang Qiu, Ji Liu

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers.

no code implementations • 8 Oct 2018 • Yawei Zhao, Shuang Qiu, Ji Liu

While the online gradient method has been shown to be optimal for the static regret metric, the optimal algorithm for the dynamic regret remains unknown.

no code implementations • 27 Sep 2018 • Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.

no code implementations • 28 May 2018 • Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu

Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.

no code implementations • 17 Mar 2017 • Shuang Qiu, Tingjin Luo, Jieping Ye, Ming Lin

We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels.

no code implementations • 2 Mar 2017 • Ming Lin, Shuang Qiu, Bin Hong, Jieping Ye

We show that the conventional gradient descent heuristic is biased by the skewness of the distribution therefore is no longer the best practice of learning the SLM.

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