no code implementations • 9 Apr 2024 • Rui Cai, Shichao Pei, Xiangliang Zhang
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively studied, exploring it within a multimodal KGC context presents distinct challenges and opportunities.
1 code implementation • 16 Feb 2024 • Shengzhi Li, Rongyu Lin, Shichao Pei
In conclusion, we propose a distillation-based multi-modal alignment model with fine-grained annotations on a small dataset that reconciles the textual and visual performance of MLLMs, restoring and boosting language capability after visual instruction tuning.
1 code implementation • 21 Jan 2024 • Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges.
no code implementations • 7 Oct 2023 • Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan, Kehan Guo, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.
1 code implementation • 23 Apr 2023 • Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
no code implementations • 1 Feb 2023 • Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla
Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.
no code implementations • 29 May 2022 • Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
no code implementations • 2 May 2022 • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
no code implementations • 9 Oct 2021 • Xin Huang, Xuejiao Tang, Wenbin Zhang, Shichao Pei, Ji Zhang, Mingli Zhang, Zhen Liu, Ruijun Chen, Yiyi Huang
The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system.
no code implementations • 29 Sep 2021 • Lu Yu, Shichao Pei, Chuxu Zhang, Xiangliang Zhang
Pairwise ranking models have been widely used to address various problems, such as recommendation.
no code implementations • 7 Jun 2021 • Basmah Altaf, Shichao Pei, Xiangliang Zhang
Data intensive research requires the support of appropriate datasets.
no code implementations • 2 Sep 2020 • Lu Yu, Shichao Pei, Lizhong Ding, Jun Zhou, Longfei Li, Chuxu Zhang, Xiangliang Zhang
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.
no code implementations • 19 May 2020 • Lu Yu, Shichao Pei, Chuxu Zhang, Shangsong Liang, Xiao Bai, Nitesh Chawla, Xiangliang Zhang
Pairwise ranking models have been widely used to address recommendation problems.