no code implementations • 23 Mar 2023 • Min Gao, Tristan T. Hormel, Yukun Guo, Kotaro Tsuboi, Christina J. Flaxel, David Huang, Thomas S. Hwang, Yali Jia
While not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP.
no code implementations • 10 Feb 2023 • Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo
Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.
2 code implementations • 19 Oct 2022 • Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, Hongzhi Yin
To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.
2 code implementations • 23 Jun 2022 • Chen Lin, Si Chen, Meifang Zeng, Sheng Zhang, Min Gao, Hui Li
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
no code implementations • 8 Mar 2022 • Yinghui Tao, Min Gao, Junliang Yu, Zongwei Wang, Qingyu Xiong, Xu Wang
To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.
no code implementations • 19 Feb 2022 • Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin
By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.
1 code implementation • 9 Sep 2021 • Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin
Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.
no code implementations • 22 Jul 2021 • Fan Wu, Min Gao, Junliang Yu, Zongwei Wang, Kecheng Liu, Xu Wange
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
1 code implementation • 7 Jun 2021 • Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
1 code implementation • 29 Jan 2021 • Yifan Wu, Min Gao, Min Zeng, Feiyang Chen, Min Li, Jie Zhang
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
no code implementations • 23 Dec 2020 • Jie Li, Binglin Li, Min Gao
Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation.
no code implementations • Knowledge Based Systems 2020 • Chao Wu, Qingyu Xiong, Hualing Yi, Yang Yu, Qiwu Zhu, Min Gao, Jie Chen
In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.
no code implementations • 18 Oct 2020 • Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu
We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
no code implementations • 17 Aug 2020 • Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
1 code implementation • 10 Aug 2020 • Junwei Zhang, Min Gao, Junliang Yu, Linda Yang, Zongwei Wang, Qingyu Xiong
Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited.
no code implementations • 19 Apr 2020 • Min Gao, Yukun Guo, Tristan T. Hormel, Jiande Sun, Thomas Hwang, Yali Jia
The reconstructed 6x6-mm angiograms have significantly lower noise intensity and better vascular connectivity than the original images.
no code implementations • 5 Apr 2020 • Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.
no code implementations • 5 Mar 2020 • Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, Qingyu Xiong
In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.
no code implementations • 8 Sep 2019 • Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.