no code implementations • 11 Jan 2024 • Jiaheng Xie, Ruicheng Liang, Yidong Chai, Yang Liu, Daniel Zeng
To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health.
1 code implementation • 24 Dec 2023 • Xinglin Xiao, Yijie Wang, Nan Xu, Yuqi Wang, Hanxuan Yang, Minzheng Wang, Yin Luo, Lei Wang, Wenji Mao, Daniel Zeng
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
no code implementations • 13 Dec 2023 • Xingjin Wang, Linjing Li, Daniel Zeng
With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence.
no code implementations • 2 Aug 2023 • Haorui Li, Jiaqi Liang, Linjing Li, Daniel Zeng
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers.
no code implementations • NeurIPS 2023 • Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, Jure Leskovec
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
no code implementations • 4 Jul 2022 • Daniel Zeng, Tailin Wu, Jure Leskovec
Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy.
1 code implementation • 23 May 2022 • Jiazhi Xu, Sheng Huang, Fengtao Zhou, Luwen Huangfu, Daniel Zeng, Bo Liu
Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels.
no code implementations • 4 Mar 2022 • Yanwu Yang, Xin Li, Bernard J. Jansen, Daniel Zeng
Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.
no code implementations • 28 Feb 2022 • Yanwu Yang, Baozhu Feng, Daniel Zeng
The GVW model and its deep learning-based estimation method provide a basis to support big data-driven advertising analytics and decision makings; in the meanwhile, identified properties and experimental findings of this research illuminate critical managerial insights for advertisers in various advertising forms.
no code implementations • 28 Feb 2022 • Yanwu Yang, Bernard J. Jansen, Yinghui Yang, Xunhua Guo, Daniel Zeng
This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions.
no code implementations • COLING 2020 • Zikang Wang, Linjing Li, Daniel Zeng
In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI.
no code implementations • 30 Sep 2019 • Jie Bai, Linjing Li, Daniel Zeng
Inspired by a cognitive model of human memory, we propose a network representation learning scheme.
no code implementations • 27 Sep 2013 • Tianjun Fu, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.