Zobrazeno 1 - 10
of 405
pro vyhledávání: '"Yuan, Geng"'
Autor:
Zhan, Zheng, Wu, Yushu, Gong, Yifan, Meng, Zichong, Kong, Zhenglun, Yang, Changdi, Yuan, Geng, Zhao, Pu, Niu, Wei, Wang, Yanzhi
The rapid progress in artificial intelligence-generated content (AIGC), especially with diffusion models, has significantly advanced development of high-quality video generation. However, current video diffusion models exhibit demanding computational
Externí odkaz:
http://arxiv.org/abs/2411.01171
Autor:
Liu, Jun, Yuan, Geng, Zeng, Weihao, Tang, Hao, Zhang, Wenbin, Lin, Xue, Xu, XiaoLin, Huang, Dong, Wang, Yanzhi
Publikováno v:
Springer Nature - Book Series: Transactions on Computational Science & Computational Intelligence, 2022
In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planning and patient follow-up. This study aims to utilize a specially MRI-b
Externí odkaz:
http://arxiv.org/abs/2409.19583
Autor:
Wu, Chao, Gong, Yifan, Liu, Liangkai, Li, Mengquan, Wu, Yushu, Shen, Xuan, Li, Zhimin, Yuan, Geng, Shi, Weisong, Wang, Yanzhi
Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad application prospects. However, the development of this field is rigorously restricted by the deployment dilemma of simultaneously achieving high accuracy, excellent
Externí odkaz:
http://arxiv.org/abs/2408.05363
Autor:
Xie, Yanyue, Dong, Peiyan, Yuan, Geng, Li, Zhengang, Zabihi, Masoud, Wu, Chao, Chang, Sung-En, Zhang, Xufeng, Lin, Xue, Ding, Caiwen, Yoshikawa, Nobuyuki, Chen, Olivia, Wang, Yanzhi
Superconducting circuits, like Adiabatic Quantum-Flux-Parametron (AQFP), offer exceptional energy efficiency but face challenges in physical design due to sophisticated spacing and timing constraints. Current design tools often neglect the importance
Externí odkaz:
http://arxiv.org/abs/2407.18209
Autor:
Wang, Tianyu, Li, Sheng, Li, Bingyao, Dai, Yue, Li, Ao, Yuan, Geng, Ding, Yufei, Zhang, Youtao, Tang, Xulong
Continuous learning (CL) has emerged as one of the most popular deep learning paradigms deployed in modern cloud GPUs. Specifically, CL has the capability to continuously update the model parameters (through model retraining) and use the updated mode
Externí odkaz:
http://arxiv.org/abs/2407.13126
Autor:
Liu, Jun, Wu, Chao, Yang, Changdi, Tang, Hao, Kong, Zhenglun, Yuan, Geng, Niu, Wei, Huang, Dong, Wang, Yanzhi
Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices. Structured pruning is a widely used method to
Externí odkaz:
http://arxiv.org/abs/2403.10799
There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model training process is both time-intensive and energy-intensive, inevitably
Externí odkaz:
http://arxiv.org/abs/2401.16720
Autor:
Li, Sheng, Yuan, Geng, Wu, Yawen, Dai, Yue, Wang, Tianyu, Wu, Chao, Jones, Alex K., Hu, Jingtong, Wang, Yanzhi, Tang, Xulong
Many emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and require the deployment of DNN models on edge devices. These applications naturally require i) handling stre
Externí odkaz:
http://arxiv.org/abs/2401.16694
Autor:
Li, Bingbing, Yuan, Geng, Wang, Zigeng, Huang, Shaoyi, Peng, Hongwu, Behnam, Payman, Wen, Wujie, Liu, Hang, Ding, Caiwen
Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication. However, hardware failures, such as stuck-at-fault defects, can result in
Externí odkaz:
http://arxiv.org/abs/2401.11664
Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented
Externí odkaz:
http://arxiv.org/abs/2310.12451