no code implementations • 24 May 2024 • Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen, Ying Cui, Dongyan Guo
Besides, the experiments also show that the precise camera intrinsic and depth maps estimated by our pipeline can greatly benefit practical applications such as 3D reconstruction from a single in-the-wild image.
1 code implementation • 13 May 2024 • Jake Roth, Ying Cui
Superquantiles have recently gained significant interest as a risk-aware metric for addressing fairness and distribution shifts in statistical learning and decision making problems.
no code implementations • 12 Oct 2023 • Qiuhong Wei, Zhengxiong Yao, Ying Cui, Bo Wei, Zhezhen Jin, Ximing Xu
Large language models such as ChatGPT are increasingly explored in medical domains.
1 code implementation • 13 Jun 2023 • Yangchen Li, Ying Cui, Vincent Lau
In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers.
no code implementations • 9 May 2023 • Chuanfei Hu, Tianyi Xia, Ying Cui, Quchen Zou, Yuancheng Wang, Wenbo Xiao, Shenghong Ju, Xinde Li
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically.
no code implementations • 23 Mar 2023 • Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, Ju Sun
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
no code implementations • 21 Oct 2022 • Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun
We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification.
no code implementations • 2 Oct 2022 • Hengyue Liang, Buyun Liang, Ying Cui, Tim Mitchell, Ju Sun
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems.
no code implementations • 6 Jun 2022 • Qi Wang, Ying Cui, Chenglin Li, Junni Zou, Hongkai Xiong
To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy.
1 code implementation • 16 Mar 2022 • Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, Chunhua Shen
To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs.
no code implementations • 26 Nov 2021 • Yangchen Li, Ying Cui, Vincent Lau
To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence.
no code implementations • 1 Nov 2021 • Hewon Cho, Ying Cui, Jemin Lee
Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles.
no code implementations • 25 Oct 2021 • Yangchen Li, Ying Cui, Vincent Lau
Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint.
1 code implementation • 13 Apr 2021 • Ying Cui, Yangchen Li, Chencheng Ye
We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively.
no code implementations • 17 Mar 2021 • Chencheng Ye, Ying Cui
In this paper, we investigate unconstrained and constrained sample-based federated optimization, respectively.
no code implementations • CVPR 2021 • Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen
We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.
no code implementations • 5 Aug 2020 • Ying Cui, Shuaichao Li, Wanqing Zhang
Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT).
no code implementations • 3 Jul 2020 • Miao Tian, Dongyan Guo, Ying Cui, Xiang Pan, Sheng-Yong Chen
Novelty detection is a important research area which mainly solves the classification problem of inliers which usually consists of normal samples and outliers composed of abnormal samples.
2 code implementations • CVPR 2020 • Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Sheng-Yong Chen
The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction.
no code implementations • 6 Oct 2019 • Zhengling Qi, Ying Cui, Yufeng Liu, Jong-Shi Pang
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a class of coupled nonconvex and nonsmoothempirical risk minimization problems, and (b) validate these properties by a noisy amplitude-based phase retrieval problem, the latter being of much topical interest. Derived from available data via sampling, these empirical risk minimization problems are the computational workhorse of a population risk model which involves the minimization of an expected value of a random functional.
no code implementations • 27 Aug 2019 • Zhengling Qi, Ying Cui, Yufeng Liu, Jong-Shi Pang
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments.
1 code implementation • 3 Jun 2019 • Shuai Wang, Tsung-Hui Chang, Ying Cui, Jong-Shi Pang
We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable.
no code implementations • 4 Feb 2019 • Dongyan Guo, Jun Wang, Weixuan Zhao, Ying Cui, Zhenhua Wang, Sheng-Yong Chen
Both features and the channel weights are utilized in a template generation layer to generate a discriminative template.