Zobrazeno 1 - 10
of 332
pro vyhledávání: '"Zhu, Zeyu"'
Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution to train GNN on large graphs with billions of nodes and edges, the sa
Externí odkaz:
http://arxiv.org/abs/2409.14939
Autor:
Chen, Tianqi, Li, Zhe, Xu, Weixiang, Zhu, Zeyu, Li, Dong, Tian, Lu, Barsoum, Emad, Wang, Peisong, Cheng, Jian
Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements. Ternarization, an extreme form of quantization, offers a solution
Externí odkaz:
http://arxiv.org/abs/2406.07177
Autor:
Zhu, Zeyu, Li, Fanrong, Li, Gang, Liu, Zejian, Mo, Zitao, Hu, Qinghao, Liang, Xiaoyao, Cheng, Jian
Graph Neural Networks (GNNs) are becoming a promising technique in various domains due to their excellent capabilities in modeling non-Euclidean data. Although a spectrum of accelerators has been proposed to accelerate the inference of GNNs, our anal
Externí odkaz:
http://arxiv.org/abs/2311.09775
Autor:
Yao, Xingting, Hu, Qinghao, Liu, Tielong, Mo, Zitao, Zhu, Zeyu, Zhuge, Zhengyang, Cheng, Jian
Spiking neural networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields (NeRF) render high-qualit
Externí odkaz:
http://arxiv.org/abs/2309.10987
Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hyperspectral (HRHS) image. Existing Bayesian-based HS pansharpening met
Externí odkaz:
http://arxiv.org/abs/2305.10925
Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each p
Externí odkaz:
http://arxiv.org/abs/2303.13659
Autor:
Zhu, Zeyu, Li, Fanrong, Mo, Zitao, Hu, Qinghao, Li, Gang, Liu, Zejian, Liang, Xiaoyao, Cheng, Jian
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, m
Externí odkaz:
http://arxiv.org/abs/2302.00193
Autor:
Zhu, Zeyu
In this thesis, we consider a rational inattentive agent who does not observe the environment perfectly and needs to acquire costly signal to make decisions. By observing agents actions, we formulate the inverse rational inattention framework to reco
Externí odkaz:
https://hdl.handle.net/2144/44472
Publikováno v:
In European Journal of Pharmacology 5 November 2024 982
Publikováno v:
In Food Chemistry 15 February 2025 465 Part 2