Huawei Noah's Ark LabThe Noah’s Ark Lab is the AI research center for Huawei Technologies, located in Hong Kong, Shenzhen, Beijing, Shanghai, Xi’an, London, Paris, Toronto, Montreal, Edmonton, etc. The mission of the lab is to make significant contributions to both the company and society by innovating in artificial intelligence, data mining and related fields. Mainly driven by long term and big impact projects, research in the lab also tries to advance the state of the art in the fields as well as to harness the products and services of the company, at each stage of the innovation process. As a world class research lab, we are pushing the frontier of research and development in all areas that we work in.We dare to address both the challenges and opportunities in this era of AI and big data, to revolutionize the ways in which people work and live, and the ways in which companies do business, through intelligentization of all processes, with the slogan 'from big data to deep knowledge'. Research areas of the lab mainly include computer vision, natural language processing, search & recommendation, decision and reasoning ,AI theory. Founded in 2012, the lab has now grown to be a research organization with many significant achievements in both academia and industry. We welcome talented researchers and engineers to join us to realize their dreams.
Minghao Xu, Jian Zhang,Bingbing Ni, Teng Li, Chengjie Wang,Qi Tian,Wenjun Zhang
national conference on artificial intelligence, (2020)
In order to facilitate a more continuous domain-invariant latent space and fully utilize the inter-domain information, we propose the domain mixup on pixel and feature level
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2636-2645, (2020)
The proposed methods have been deployed onto the training platform of Huawei App Store recommendation service, with significant economic profit demonstrated
Cited by4BibtexViews233Links
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Yichun Yin,Lifeng Shang,Xin Jiang, Xiao Chen,Qun Liu
national conference on artificial intelligence, (2020)
We have proposed a reinforced data augmentation method for dialogue state tracking in order to improve its performance by generating high-quality training data
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We propose the novel Q-value Attention network for the multiagent Q-value decomposition problem
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national conference on artificial intelligence, (2020)
A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explos...
Cited by3BibtexViews144Links
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Yong Liu,Weixun Wang, Yujing Hu,Jianye Hao, Xingguo Chen,Yang Gao
national conference on artificial intelligence, (2020)
We propose a novel two-stage attention mechanism G2ANet for game abstraction, which can be combined with graph neural network
Cited by2BibtexViews189Links
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Hangyu Mao,Wulong Liu,Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang,Zhen Xiao
national conference on artificial intelligence, (2020)
We propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations
Cited by2BibtexViews173Links
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Antoine Yang, Pedro M. Esperança, Fabio M. Carlucci
ICLR, (2020)
In this paper we have shown that, for many Neural Architecture Search methods, the search space has been engineered such that all architectures perform well and that their relative ranking can shift
Cited by2BibtexViews147Links
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CVPR, pp.14451-14460, (2020)
Local user-adaptation with a data regularization approach based on adaptive Batch Normalization, and especially its supervised variant, seem more promising, leading to systematic improvements when taking advantage of labeled user-specific data
Cited by1BibtexViews166Links
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IEEE Transactions on Mobile Computing, pp.1-1, (2020)
Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection c...
Cited by1BibtexViews99Links
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Kristopher De Asis, Alan Chan,Silviu Pitis,Richard Sutton, Daniel Graves
national conference on artificial intelligence, (2020)
We argued that fixed-horizon TD agents are stable under function approximation and have additional predictive power
Cited by1BibtexViews106Links
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Lewei Yao,Hang Xu, Wei Zhang,Xiaodan Liang,Zhenguo Li
AAAI, pp.12661-12668, (2020)
We propose a detection Neural architecture search framework for searching both an efficient combination of modules and better modular-level architectures for object detection on a target device
Cited by1BibtexViews154Links
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Yehui Tang,Yunhe Wang,Yixing Xu,Boxin Shi,Chao Xu, Chunjing Xu,Chang Xu
national conference on artificial intelligence, (2020)
We investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks and propose a feature distortion method for addressing the aforementioned problem
Cited by1BibtexViews180Links
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Zifeng Wang, Hong Zhu, Zhenhua Dong,Xiuqiang He,Shao-Lun Huang
national conference on artificial intelligence, (2020)
We theoretically study the unweighted subsampling with influence function, propose a novel unweighted subsampling framework and design a family of probabilistic sampling methods
Cited by1BibtexViews187Links
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Haifeng Zhang, Weizhe Chen, Zeren Huang, Minne Li,Yaodong Yang,Weinan Zhang,Jun Wang
national conference on artificial intelligence, (2020)
Our experiments on matrix games and a highway merge environment demonstrate the effectiveness of our algorithm to find the Stackelberg solutions which outperform the state-of-the-art baselines
Cited by1BibtexViews117Links
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Guibing Guo, Bowei Chen, Xiaoyan Zhang, Zhirong Liu, Zhenhua Dong,Xiuqiang He
national conference on artificial intelligence, (2020)
We proposed a two-level attentive neural network called TAAS to capture the semantic correlation between title and abstract for paper recommendation
Cited by1BibtexViews165Links
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Yimin Huang,Weiran Huang, Liang Li,Zhenguo Li
national conference on artificial intelligence, (2020)
Moral-Benito already pointed out the concern, “From a pure empirical viewpoint, model uncertainty represents a concern because estimates may well depend on the particular model considered.” combining multiple models to reduce the model uncertainty is very desirable
Cited by1BibtexViews117Links
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ICLR, (2020)
We develop a decentralized adversarial imitation learning algorithm with correlated policies with approximated opponents modeling
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We propose a novel multiagent transfer learning framework for efficient multiagent learning by taking advantage of option-based policy transfer
Cited by1BibtexViews171Links
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Lujia Pan, Jianfeng Zhang,Patrick P.C. Lee, Marcus Kalander, Junjian Ye,Pinghui Wang
(2020): 106969
We show via evaluation that the features related to the network topology are crucial for anomaly detection and enable PMADS to achieve both high precision and high recall
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