Search Results for author: Shichao Pei

Found 13 papers, 3 papers with code

Zero-Shot Relational Learning for Multimodal Knowledge Graphs

no code implementations9 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.

Relational Reasoning

Multi-modal preference alignment remedies regression of visual instruction tuning on language model

1 code implementation16 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.

Instruction Following Language Modelling +2

Large Language Model based Multi-Agents: A Survey of Progress and Challenges

1 code implementation21 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.

Decision Making Language Modelling +1

Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout

no code implementations7 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.

LogicRec: Recommendation with Users' Logical Requirements

1 code implementation23 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.

Knowledge Graphs Recommendation Systems +1

Knowledge Distillation on Graphs: A Survey

no code implementations1 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.

Knowledge Distillation Model Compression

TAR: Neural Logical Reasoning across TBox and ABox

no code implementations29 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.

Descriptive Logical Reasoning +1

A Generic Knowledge Based Medical Diagnosis Expert System

no code implementations9 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.

Medical Diagnosis

A Simple and Debiased Sampling Method for Personalized Ranking

no code implementations29 Sep 2021 Lu Yu, Shichao Pei, Chuxu Zhang, Xiangliang Zhang

Pairwise ranking models have been widely used to address various problems, such as recommendation.

SAIL: Self-Augmented Graph Contrastive Learning

no code implementations2 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.

Contrastive Learning Knowledge Distillation +1

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