1 code implementation • 24 Feb 2024 • Cai Xu, Jiajun Si, Ziyu Guan, Wei Zhao, Yue Wu, Xiyue Gao
To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data.
1 code implementation • 20 Dec 2023 • Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Long Jin
However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue.
no code implementations • 29 Sep 2023 • Jie Zhao, Ziyu Guan, Wei Zhao, Yue Jiang, Xiaofei He
Recent works considering professional legal-linguistic style (PLLS) texts have shown promising results on the charge prediction task.
no code implementations • 19 Feb 2023 • Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Yuanhai Lv, Lining Xing, Baosheng Yu, DaCheng Tao
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions.
1 code implementation • 19 Oct 2022 • Yaming Yang, Ziyu Guan, Zhe Wang, Wei Zhao, Cai Xu, Weigang Lu, Jianbin Huang
The two modules can effectively utilize and enhance each other, promoting the model to learn discriminative embeddings.
no code implementations • 13 Aug 2022 • Yiheng Lu, Ziyu Guan, Yaming Yang, Maoguo Gong, Wei Zhao, Kaiyuan Feng
By leveraging the proposed AFIE, the proposed framework is able to yield a stable importance evaluation of each filter no matter whether the original model is trained fully.
no code implementations • 22 Dec 2021 • Weigang Lu, Yibing Zhan, Binbin Lin, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao
In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.
4 code implementations • 28 Aug 2021 • Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, DaCheng Tao
The experimental results provide sound empirical evidence on the superiority of learning from diverse animals species in terms of both accuracy and generalization ability.
no code implementations • CVPR 2021 • Shaobo Zhang, Wanqing Zhao, Ziyu Guan, Xianlin Peng, Jinye Peng
Hence, we propose to use the domain-invariant geometry structure among keypoints as a "bridge" constraint to optimize DAKDN for 6D pose estimation across domains.
no code implementations • 8 Jun 2021 • Ziyu Guan, Hongchang Wu, Qingyu Cao, Hao liu, Wei Zhao, Sheng Li, Cai Xu, Guang Qiu, Jian Xu, Bo Zheng
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
no code implementations • 18 May 2021 • Ruijing Yang, Ziyu Guan, Zitong Yu, Xiaoyi Feng, Jinye Peng, Guoying Zhao
The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task.
1 code implementation • 27 May 2020 • Yaming Yang, Ziyu Guan, Jian-Xin Li, Wei Zhao, Jiangtao Cui, Quan Wang
However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity.
no code implementations • 24 Jul 2019 • Wei Zhao, Boxuan Zhang, Beidou Wang, Ziyu Guan, Wanxian Guan, Guang Qiu, Wei Ning, Jiming Chen, Hongmin Liu
(2) It is difficult to obtain users' explicit feedback of their preference in product features.
1 code implementation • 24 Jul 2019 • Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He
In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.
1 code implementation • 24 Jun 2019 • Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, Deng Cai
A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.
1 code implementation • 17 May 2019 • Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu
This paper targets to a novel but practical recommendation problem named exact-K recommendation.
no code implementations • 23 May 2018 • Yu Zhu, Jinhao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai
Both content information (e. g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item.
no code implementations • 23 May 2018 • Yu Zhu, Junxiong Zhu, Jie Hou, Yongliang Li, Beidou Wang, Ziyu Guan, Deng Cai
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality.
no code implementations • 1 Mar 2018 • Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, Xiaofei He
In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB.
no code implementations • EMNLP 2017 • Jie Zhao, Yu Su, Ziyu Guan, Huan Sun
Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers.