Zobrazeno 1 - 10
of 24 707
pro vyhledávání: '"An, Zhidong"'
PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection
Single point supervised oriented object detection has gained attention and made initial progress within the community. Diverse from those approaches relying on one-shot samples or powerful pretrained models (e.g. SAM), PointOBB has shown promise due
Externí odkaz:
http://arxiv.org/abs/2410.08210
Despite demonstrating superior performance across a variety of linguistic tasks, pre-trained large language models (LMs) often require fine-tuning on specific datasets to effectively address different downstream tasks. However, fine-tuning these LMs
Externí odkaz:
http://arxiv.org/abs/2410.00362
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been deployed in many real-world applications. However, deploying FL over mo
Externí odkaz:
http://arxiv.org/abs/2410.10833
Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a solution, enabling collaborative model training without users directly sh
Externí odkaz:
http://arxiv.org/abs/2409.19513
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. To mitigate this issue, Hier
Externí odkaz:
http://arxiv.org/abs/2409.19509
Autor:
Li, Ming-Jian, Lian, Yanping, Cheng, Zhanshan, Li, Lehui, Wang, Zhidong, Gao, Ruxin, Fang, Daining
Numerical simulation is powerful to study nonlinear solid mechanics problems. However, mesh-based or particle-based numerical methods suffer from the common shortcoming of being time-consuming, particularly for complex problems with real-time analysi
Externí odkaz:
http://arxiv.org/abs/2409.10572
Autor:
Wang, Suzhen, Ma, Yifeng, Ding, Yu, Hu, Zhipeng, Fan, Changjie, Lv, Tangjie, Deng, Zhidong, Yu, Xin
Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personalized characteristics and therefore fail to produce diverse speaking sty
Externí odkaz:
http://arxiv.org/abs/2409.09292
Generative self-supervised learning (SSL), especially masked autoencoders (MAE), has greatly succeeded and garnered substantial research interest in graph machine learning. However, the research of MAE in dynamic graphs is still scant. This gap is pr
Externí odkaz:
http://arxiv.org/abs/2409.09262
Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distrib
Externí odkaz:
http://arxiv.org/abs/2409.04022
Autor:
Knottenbelt, William, Gao, Zeyu, Wray, Rebecca, Zhang, Woody Zhidong, Liu, Jiashuai, Crispin-Ortuzar, Mireia
Survival analysis is a branch of statistics used for modeling the time until a specific event occurs and is widely used in medicine, engineering, finance, and many other fields. When choosing survival models, there is typically a trade-off between pe
Externí odkaz:
http://arxiv.org/abs/2409.04290