Zobrazeno 1 - 10
of 2 026
pro vyhledávání: '"Lin,Ning"'
Equivariant Graph Neural Networks (GNNs) that incorporate E(3) symmetry have achieved significant success in various scientific applications. As one of the most successful models, EGNN leverages a simple scalarization technique to perform equivariant
Externí odkaz:
http://arxiv.org/abs/2410.11443
Computing-in-memory (CIM) is an emerging computing paradigm, offering noteworthy potential for accelerating neural networks with high parallelism, low latency, and energy efficiency compared to conventional von Neumann architectures. However, existin
Externí odkaz:
http://arxiv.org/abs/2409.18841
Autor:
Wang, Bo, Wang, Shaocong, Lin, Ning, Li, Yi, Yu, Yifei, Zhang, Yue, Yang, Jichang, Wu, Xiaoshan, He, Yangu, Wang, Songqi, Chen, Rui, Li, Guoqi, Qi, Xiaojuan, Wang, Zhongrui, Shang, Dashan
There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital computers. However, they still cannot parallel human brains in terms of
Externí odkaz:
http://arxiv.org/abs/2407.18625
Autor:
Wong, Kwunhang, Wang, Songqi, Huang, Wei, Zhang, Xinyuan, He, Yangu, Lai, Karl M. H., Jiao, Yuzhong, Lin, Ning, Qi, Xiaojuan, Chen, Xiaoming, Wang, Zhongrui
Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications as compared to artificial neural networks (ANNs). However, t
Externí odkaz:
http://arxiv.org/abs/2407.15152
Autor:
Zhang, Yue, Zhang, Woyu, Wang, Shaocong, Lin, Ning, Yu, Yifei, He, Yangu, Wang, Bo, Jiang, Hao, Lin, Peng, Xu, Xiaoxin, Qi, Xiaojuan, Wang, Zhongrui, Zhang, Xumeng, Shang, Dashan, Liu, Qi, Cheng, Kwang-Ting, Liu, Ming
The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digita
Externí odkaz:
http://arxiv.org/abs/2407.08990
Quantifying cascading power outages during climate extremes considering renewable energy integration
Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which com
Externí odkaz:
http://arxiv.org/abs/2407.01758
Autor:
Lin, Ning, Wang, Shaocong, Zhang, Yue, He, Yangu, Wong, Kwunhang, Basu, Arindam, Shang, Dashan, Chen, Xiaoming, Wang, Zhongrui
Deep neural networks (DNNs), such as the widely-used GPT-3 with billions of parameters, are often kept secret due to high training costs and privacy concerns surrounding the data used to train them. Previous approaches to securing DNNs typically requ
Externí odkaz:
http://arxiv.org/abs/2406.14863
Autor:
Chen, Hegan, Yang, Jichang, Chen, Jia, Wang, Songqi, Wang, Shaocong, Wang, Dingchen, Tian, Xinyu, Yu, Yifei, Chen, Xi, Lin, Yinan, He, Yangu, Wu, Xiaoshan, Li, Yi, Zhang, Xinyuan, Lin, Ning, Xu, Meng, Zhang, Xumeng, Wang, Zhongrui, Wang, Han, Shang, Dashan, Liu, Qi, Cheng, Kwang-Ting, Liu, Ming
Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer models, revolutionising fields such as manufacturing management and industrial automation. Recent advances in machine learning provide data-driven methods
Externí odkaz:
http://arxiv.org/abs/2406.08343
Autor:
Yu, Yifei, Wang, Shaocong, Zhang, Woyu, Zhang, Xinyuan, Wu, Xiuzhe, He, Yangu, Yang, Jichang, Zhang, Yue, Lin, Ning, Wang, Bo, Chen, Xi, Wang, Songqi, Zhang, Xumeng, Qi, Xiaojuan, Wang, Zhongrui, Shang, Dashan, Liu, Qi, Cheng, Kwang-Ting, Liu, Ming
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical ima
Externí odkaz:
http://arxiv.org/abs/2404.09613
Autor:
Yang, Jichang, Chen, Hegan, Chen, Jia, Wang, Songqi, Wang, Shaocong, Yu, Yifei, Chen, Xi, Wang, Bo, Zhang, Xinyuan, Cui, Binbin, Li, Yi, Lin, Ning, Xu, Meng, Xu, Xiaoxin, Qi, Xiaojuan, Wang, Zhongrui, Zhang, Xumeng, Shang, Dashan, Wang, Han, Liu, Qi, Cheng, Kwang-Ting, Liu, Ming
Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidi
Externí odkaz:
http://arxiv.org/abs/2404.05648