1 code implementation • 15 Dec 2021 • Yu Gong, Zhihan Xu, Zhezhi He, Weifeng Zhang, Xiaobing Tu, Xiaoyao Liang, Li Jiang
From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations.
1 code implementation • CVPR 2022 • Yang Liu, Weifeng Zhang, Chao Xiang, Tu Zheng, Deng Cai, Xiaofei He
Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks not seen during training, given only a few examples.
no code implementations • ICLR 2021 • Lin Ning, Guoyang Chen, Weifeng Zhang, Xipeng Shen
This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning.
no code implementations • 31 Aug 2020 • Jing Yu, Zihao Zhu, Yujing Wang, Weifeng Zhang, Yue Hu, Jianlong Tan
Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts.
no code implementations • 17 Feb 2020 • Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, Yang Ning
In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier.
1 code implementation • 12 Jun 2019 • Xinli Cai, Peng Zhou, Shuhan Ding, Guoyang Chen, Weifeng Zhang
Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage.
no code implementations • 18 Mar 2019 • Ye Yu, Yingmin Li, Shuai Che, Niraj K. Jha, Weifeng Zhang
It models the accelerator design task as a multi-dimensional optimization problem.
no code implementations • 30 Jan 2019 • Hongxu Yin, Guoyang Chen, Yingmin Li, Shuai Che, Weifeng Zhang, Niraj K. Jha
In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models.
no code implementations • 3 Feb 2018 • Jing Yu, Yuhang Lu, Zengchang Qin, Yanbing Liu, Jianlong Tan, Li Guo, Weifeng Zhang
A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval.