no code implementations • 27 Mar 2024 • Xiaofeng Wu, Velibor Bojkovic, Bin Gu, Kun Suo, Kai Zou
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes.
no code implementations • 30 Apr 2023 • Zhe Chen, Yang Yang, Anne Bettens, Youngho Eun, Xiaofeng Wu
In our framework, by making the best use of the hardware parameters of the sensor that captures real-world space images, we first develop a high-fidelity RSO simulator that can generate various realistic space images.
no code implementations • 16 Feb 2023 • Rohan Agarwal, Wei Zhou, Xiaofeng Wu, Yuhan Li
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
no code implementations • 30 Aug 2021 • Bo Zhang, Tao Chen, Bin Wang, Xiaofeng Wu, Liming Zhang, Jiayuan Fan
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
no code implementations • 6 Dec 2019 • Haolin Fei, Xiaofeng Wu, Chunbo Luo
Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations.
no code implementations • 20 Nov 2017 • Weijia Chen, Yuedong Xu, Xiaofeng Wu
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.
no code implementations • COLING 2016 • Jian Zhang, Xiaofeng Wu, Andy Way, Qun Liu
We show that the neural LM perplexity can be reduced by 7. 395 and 12. 011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively.
no code implementations • LREC 2016 • Xiaofeng Wu, Jinhua Du, Qun Liu, Andy Way
This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool.
no code implementations • 10 Aug 2015 • Hui Yu, Xiaofeng Wu, Wenbin Jiang, Qun Liu, ShouXun Lin
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
no code implementations • 9 Aug 2015 • Hui Yu, Xiaofeng Wu, Wenbin Jiang, Qun Liu, ShouXun Lin
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.