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pro vyhledávání: '"Zhang, Pengyu"'
Autor:
Zhang, Pengyu, Yin, Hao, Wang, Zeren, Chen, Wenyue, Li, Shengming, Wang, Dong, Lu, Huchuan, Jia, Xu
Sign language is one of the most effective communication tools for people with hearing difficulties. Most existing works focus on improving the performance of sign language tasks on RGB videos, which may suffer from degraded recording conditions, suc
Externí odkaz:
http://arxiv.org/abs/2407.12593
As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential deployment. Through a comprehe
Externí odkaz:
http://arxiv.org/abs/2405.07010
Although Federated Learning (FL) enables collaborative learning in Artificial Intelligence of Things (AIoT) design, it fails to work on low-memory AIoT devices due to its heavy memory usage. To address this problem, various federated pruning methods
Externí odkaz:
http://arxiv.org/abs/2405.04765
Last Level Cache (LLC) is a precious and critical resource that impacts the performance of applications running on top of CPUs. In this paper, we reveal the significant impact of LLC on the performance of the 5G user plane function (UPF) when running
Externí odkaz:
http://arxiv.org/abs/2404.13991
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still faces two key challenges, i.e., data and resource heterogeneity. Due
Externí odkaz:
http://arxiv.org/abs/2402.16919
In online learning platforms, particularly in rapidly growing computer programming courses, addressing the thousands of students' learning queries requires considerable human cost. The creation of intelligent assistant large language models (LLMs) ta
Externí odkaz:
http://arxiv.org/abs/2402.07913
Learning to collaborate has witnessed significant progress in multi-agent reinforcement learning (MARL). However, promoting coordination among agents and enhancing exploration capabilities remain challenges. In multi-agent environments, interactions
Externí odkaz:
http://arxiv.org/abs/2312.09539
Autor:
Liu, Ruixuan, Hu, Ming, Xia, Zeke, Xia, Jun, Zhang, Pengyu, Huang, Yihao, Liu, Yang, Chen, Mingsong
Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive mobile computing devices, the performance of traditional homogeneous mo
Externí odkaz:
http://arxiv.org/abs/2311.14037
Due to the absence of fine structure and texture information, existing fusion-based few-shot image generation methods suffer from unsatisfactory generation quality and diversity. To address this problem, we propose a novel feature Equalization fusion
Externí odkaz:
http://arxiv.org/abs/2307.14638
Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Since random initial models in FL can easily result in unregulated Stoch
Externí odkaz:
http://arxiv.org/abs/2301.12193