Zobrazeno 1 - 10
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pro vyhledávání: '"Xue, Zi"'
Autor:
Xue, Zi Yu
Sparse tensor algebra is a challenging class of workloads to accelerate due to few opportunities for data reuse and varying sparsity patterns. Prior sparse tensor algebra accelerators have explored tiling sparse tensors to increase exploitable data r
Publikováno v:
56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '23), 2023
Sparse tensor algebra is a challenging class of workloads to accelerate due to low arithmetic intensity and varying sparsity patterns. Prior sparse tensor algebra accelerators have explored tiling sparse data to increase exploitable data reuse and im
Externí odkaz:
http://arxiv.org/abs/2310.00192
Publikováno v:
International Conference on Learning Representations 2022 (https://openreview.net/pdf?id=8WawVDdKqlL)
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolut
Externí odkaz:
http://arxiv.org/abs/2212.01927
Autor:
Wan-Ning Zhang, Wei-Jie Liang, Ying Zhang, Ming-Jian Liang, Ming-Juan Zhang, Qi Chen, Zhou-Pei Mo, Mei-Yi Wu, Xue-Zi Weng, Rui Han, Yong-Neng Liang, Miao-La Ke, Wen-Qian Lin
Publikováno v:
Heliyon, Vol 10, Iss 13, Pp e34220- (2024)
Background: Colorectal signet-ring cell carcinoma (SRCC) is a rare subtype of malignant adenocarcinoma, accounting for approximately 1 % of colorectal cancer (CRC) cases. Its biomarkers and molecular characteristics remain controversial, and there ar
Externí odkaz:
https://doaj.org/article/c6925577486a4ac8ab2fecde5d597bcf
Autor:
Xu, Na, Zhang, Guan-Dong, Xue, Zi-Yan, Wang, Meng-Meng, Su, Yan, Fang, Hongbao, Yu, Zheng-Hong, Liu, Hong-Ke, Lu, Hua, Su, Zhi
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497
Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for deployment in safety-critical applications, and blanket application of
Externí odkaz:
http://arxiv.org/abs/2110.08906
Autor:
Zhang, Wan-Ning, Liang, Wei-Jie, Zhang, Ying, Liang, Ming-Jian, Zhang, Ming-Juan, Chen, Qi, Mo, Zhou-Pei, Wu, Mei-Yi, Weng, Xue-Zi, Han, Rui, Liang, Yong-Neng, Ke, Miao-La, Lin, Wen-Qian
Publikováno v:
In Heliyon 15 July 2024 10(13)
Autor:
Liu, Fu-Rao, Ren, Jun-Jie, Wang, Meng-Yao, Liu, Hao-Yu, Cao, Jian-Kang, Ding, Yu, Xue, Zi-Han, Zhang, Xin-Shuang, Zhu, Yan-Ping, Sun, Yuan-Yuan
Publikováno v:
In Tetrahedron 29 June 2024 160
Autor:
Bajaj, Mohit, Chu, Lingyang, Xue, Zi Yu, Pei, Jian, Wang, Lanjun, Lam, Peter Cho-Ho, Zhang, Yong
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgra
Externí odkaz:
http://arxiv.org/abs/2107.04086
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