no code implementations • 30 Jan 2024 • Dachi Chen, Weitian Ding, Chen Liang, Chang Xu, Junwei Zhang, Majd Sakr
Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions.
no code implementations • 22 Nov 2022 • Shengshan Hu, Junwei Zhang, Wei Liu, Junhui Hou, Minghui Li, Leo Yu Zhang, Hai Jin, Lichao Sun
In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes.
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.
1 code implementation • 27 Jan 2021 • Junjie Li, Junwei Zhang, Xiaoyu Gong, Shuai Lü
Generative Adversarial Networks (GAN) is an adversarial model, and it has been demonstrated to be effective for various generative tasks.
no code implementations • 11 Nov 2020 • Junwei Zhang, Zhenghao Zhang, Shuai Han, Shuai Lü
Based on continuous control tasks with dense reward, this paper analyzes the assumption of the original Gaussian action exploration mechanism in PPO algorithm, and clarifies the influence of exploration ability on performance.
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 • 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 • 13 Dec 2019 • Shuai Lü, Shuai Han, Wenbo Zhou, Junwei Zhang
In this paper, we propose Recruitment-imitation Mechanism (RIM) for evolutionary reinforcement learning, a scalable framework that combines advantages of the three methods mentioned above.
no code implementations • 12 Jul 2019 • Yazhou Zhang, Lingling Song, Dawei Song, Peng Guo, Junwei Zhang, Peng Zhang
Existing sentiment analysis approaches are insufficient in modelling the interactions among people.
no code implementations • ICCV 2017 • Liang Mi, Wen Zhang, Junwei Zhang, Yonghui Fan, Dhruman Goradia, Kewei Chen, Eric M. Reiman, Xianfeng GU, Yalin Wang
We compute the OT from each image to a template and measure the Wasserstein distance between them.