no code implementations • 6 Dec 2024 • Xuan Chen, Tong Lu, Zhichun Wang
LLM-Align uses heuristic methods to select important attributes and relations of entities, and then feeds the selected triples of entities to an LLM to infer the alignment results.
no code implementations • 2 Aug 2024 • Zhichun Wang, Xuan Chen
Entity Alignment (EA) aims to match equivalent entities in different Knowledge Graphs (KGs), which is essential for knowledge fusion and integration.
no code implementations • CVPR 2024 • Xu Yang, Xuan Chen, Moqi Li, Kun Wei, Cheng Deng
Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains.
1 code implementation • 3 Aug 2023 • Lu Zeng, Xuan Chen, Xiaoshuang Shi, Heng Tao Shen
In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise.
no code implementations • 19 Jul 2022 • Xuan Chen, Fei Ji, Miaowen Wen, Yu Huang, Yuankun Tang, Andrew W. Eckford
In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval.
no code implementations • 17 Sep 2020 • Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta
Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.
no code implementations • 13 Jun 2019 • Hanshu Yan, Xuan Chen, Vincent Y. F. Tan, Wenhan Yang, Joe Wu, Jiashi Feng
They jointly facilitate unsupervised learning of a noise model for various noise types.
no code implementations • ECCV 2018 • Xuan Chen, Jun Hao Liew, Wei Xiong, Chee-Kong Chui, Sim-Heng Ong
In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems.