41 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shenzhen UniversityCN9 papers
- Beijing Institute of TechnologyCN6 papers
- Shenzhen Technology UniversityCN6 papers
- University of Hong KongHK3 papers
- Chinese Academy of SciencesCN2 papers
- Guangdong University of TechnologyCN2 papers
- Nanjing University of Aeronautics and AstronauticsCN2 papers
- Peking UniversityCN2 papers
- Tsinghua UniversityCN2 papers
- Zhejiang University of TechnologyCN2 papers
- Academy of Mathematics and Systems ScienceCN1 paper
- Atlas Scientific (United States)US1 paper
19 papers
DeRelayL: Sustainable Decentralized Relay Learning
Haihan Duan, Tengfei Ma, Yuyang Qin +4
In the era of big data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high f…
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Han Liu, Haotian Gao, Xiaotong Zhang +5
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective t…
WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression
Weikai Qu, Sijun Liang, Cheng Pan +6
Photographs taken in adverse weather conditions often suffer from blurriness, occlusion, and low brightness due to interference from rain, snow, and fog. These issues can significa…
FedSM: Robust Semantics-Guided Feature Mixup for Bias Reduction in Federated Learning with Long-Tail Data
Jingrui Zhang, Yimeng Xu, Shujie Li +5
Federated Learning (FL) enables collaborative model training across decentralized clients without sharing private data. However, FL suffers from biased global models due to non-IID…
CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
Zixuan Li, Binzong Geng, Jing Xiong +11
Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as te…
MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
Kai Fang, Jiangtao Deng, Chengzu Dong +4
Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges…
Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning
Runhao Zeng, Dingjie Zhou, Qiwei Liang +6
Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large languag…
Residual Hyperbolic Graph Convolution Networks
Yangkai Xue, Jindou Dai, Zhipeng Lu +2
Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smooth…
Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey
Zixuan Shangguan, Yanjie Dong, Song Guo +3
Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary…
QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing via an Online Joint Optimization Approach
Long He, Geng Sun, Zemin Sun +5
In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on ground infrastructure, may become unavailable due to infrastructure damag…
Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detection
Ruitong Liu, Yanbin Wang, Haitao Xu +4
Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, GNNs often rely on aggregating information from adjacent n…
RelJoin: Relative-cost-based Selection of Distributed Join Methods for Query Plan Optimization
F. Liang, F. C. M. Lau, H. Cui +4
Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different net…
Well-posedness of the martingale problem for super-Brownian motion with interactive branching
Lina Ji, Jie Xiong, Xu Yang
In this paper a martingale problem for super-Brownian motion with interactive branching is derived. The uniqueness of the solution to the martingale problem is obtained by using th…
Asymptotic estimate on the distance energy of lattices
Zhipeng Lu, Xianchang Meng
Since the well-known breakthrough of L. Guth and N. Katz on the Erdos distinct distances problem in the plane, mainstream of interest is aroused by their method and the Elekes-Shar…
A CCBM-based generalized GKB iterative regularization algorithm for inverse Cauchy problems
Rongfang Gong, Min Wang, Qin Huang +1
This paper examines inverse Cauchy problems that are governed by a kind of elliptic partial differential equation. The inverse problems involve recovering the missing data on an in…
Stochastic asymptotical regularization for linear inverse problems
Ye Zhang, Chuchu Chen
We introduce Stochastic Asymptotical Regularization (SAR) methods for the uncertainty quantification of the stable approximate solution of ill-posed linear-operator equations, whic…
Convergence analysis for forward and inverse problems in singularly perturbed time-dependent reaction-advection-diffusion equations
Dmitrii Chaikovskii, Ye Zhang
In this paper, by employing the asymptotic expansion method, we prove the existence and uniqueness of a smoothing solution for a time-dependent nonlinear singularly perturbed parti…
On counting centralizer subgroups of symmetric groups
Zhipeng Lu
Let be the symmetric group, and . We consider the structure of for any . We prove the permutations $…
A new class of accelerated regularization methods, with application to bioluminescence tomography
Rongfang Gong, B. Hofmann, Ye Zhang
In this paper we propose a new class of iterative regularization methods for solving ill-posed linear operator equations. The prototype of these iterative regularization methods is…