no code implementations • 8 May 2024 • Yaqi Wu, Zhihao Fan, Xiaofeng Chu, Jimmy S. Ren, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangcheng Zhou, Ruicheng Feng, Yuekun Dai, Peiqing Yang, Chen Change Loy, Senyan Xu, Zhijing Sun, Jiaying Zhu, Yurui Zhu, Xueyang Fu, Zheng-Jun Zha, Jun Cao, Cheng Li, Shu Chen, Liang Ma, Shiyang Zhou, Haijin Zeng, Kai Feng, Yongyong Chen, Jingyong Su, Xianyu Guan, Hongyuan Yu, Cheng Wan, Jiamin Lin, Binnan Han, Yajun Zou, Zhuoyuan Wu, Yuan Huang, Yongsheng Yu, Daoan Zhang, Jizhe Li, Xuanwu Yin, Kunlong Zuo, Yunfan Lu, Yijie Xu, Wenzong Ma, Weiyu Guo, Hui Xiong, Wei Yu, Bingchun Luo, Sabari Nathan, Priya Kansal
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
1 code implementation • 8 Apr 2024 • Zhengde Zhang, Yiyu Zhang, Haodong Yao, Jianwen Luo, Rui Zhao, Bo Huang, Jiameng Zhao, Yipu Liao, Ke Li, Lina Zhao, Jun Cao, Fazhi Qi, Changzheng Yuan
To address this challenge, a sophisticated large language model system named as Xiwu has been developed, allowing you switch between the most advanced foundation models and quickly teach the model domain knowledge.
no code implementations • 1 Mar 2024 • Salah Ghamizi, Jun Cao, Aoxiang Ma, Pedro Rodriguez
PowerFlowMultiNet outperforms traditional methods and other deep learning approaches in terms of accuracy and computational speed.
no code implementations • 20 Oct 2022 • Xian Qian, Kai Hu, Jiaqiang Wang, Yifeng Liu, Xingyuan Pan, Jun Cao, Mingxuan Wang
This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation.
1 code implementation • 15 Oct 2022 • Haifeng Li, Jun Cao, Jiawei Zhu, Qinyao Luo, Silu He, Xuyin Wang
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.
no code implementations • 23 Sep 2022 • Zewei Sun, Qingnan Jiang, ShuJian Huang, Jun Cao, Shanbo Cheng, Mingxuan Wang
Domain adaptation is an important challenge for neural machine translation.
no code implementations • 26 Aug 2022 • Cephas Samende, Zhong Fan, Jun Cao
Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production.
1 code implementation • 8 Apr 2022 • Rong Ye, Chengqi Zhao, Tom Ko, Chutong Meng, Tao Wang, Mingxuan Wang, Jun Cao
The training set is translated by a strong machine translation system and the test set is translated by human.
no code implementations • 1 Apr 2022 • Zhong Fan, Jun Cao, Taskin Jamal, Chris Fogwill, Cephas Samende, Zoe Robinson, Fiona Polack, Mark Ormerod, Sharon George, Adam Peacock, David Healey
We demonstrate the potential role of one of the largest at scale multi-vector Smart Energy Network Demonstrator (SEND).
no code implementations • 21 Nov 2021 • Daniel J. B. Harrold, Jun Cao, Zhong Fan
In this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading.
2 code implementations • EMNLP 2021 • Qingnan Jiang, Mingxuan Wang, Jun Cao, Shanbo Cheng, ShuJian Huang, Lei LI
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?
no code implementations • 20 Aug 2021 • Cephas Samende, Jun Cao, Zhong Fan
In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading.
no code implementations • 30 Jun 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Yu Liu, Qing Zhu, Guohua Wu
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
no code implementations • 10 Jun 2021 • Daniel J. B. Harrold, Jun Cao, Zhong Fan
As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy.
1 code implementation • ACL (IWSLT) 2021 • Chengqi Zhao, Zhicheng Liu, Jian Tong, Tao Wang, Mingxuan Wang, Rong Ye, Qianqian Dong, Jun Cao, Lei LI
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
no code implementations • 2 Mar 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Qing Zhu, Guohua Wu
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
no code implementations • 22 Dec 2020 • Zhangquan Xie, Jun Cao, Yayun Ding, Mengchao Liu, Xilei Sun, Wei Wang, Yuguang Xie
A liquid scintillator (LS) is developed for the Taishan Antineutrino Observatory (TAO), a ton-level neutrino detector to measure the reactor antineutrino spectrum with sub-percent energy resolution by adopting Silicon Photomultipliers (SiPMs) as photosensor.
Instrumentation and Detectors High Energy Physics - Experiment
no code implementations • WMT (EMNLP) 2020 • Runxin Xu, Zhuo Zhi, Jun Cao, Mingxuan Wang, Lei LI
In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions.
no code implementations • ACL 2020 • Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yu-Ping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei LI
This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation.
1 code implementation • 6 Apr 2020 • Xinglei Wang, Xuefeng Guan, Jun Cao, Na Zhang, Huayi Wu
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
no code implementations • 14 Apr 2019 • Na Zhang, Xuefeng Guan, Jun Cao, Xinglei Wang, Huayi Wu
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.