2 citations · 3 across the 7 of their papers we have counts for
5 papers
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
Ang Li, Qiangchao Chen, Yiquan Wu +4
Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have…
FedHC: A Scalable Federated Learning Framework for Heterogeneous and Resource-Constrained Clients
Min Zhang, Fuxun Yu, Yongbo Yu +3
Federated Learning (FL) is a distributed learning paradigm that empowers edge devices to collaboratively learn a global model leveraging local data. Simulating FL on GPU is essenti…
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
Tianyue Zheng, Ang Li, Zhe Chen +2
Object detection with on-board sensors (e.g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities. While…
A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network
Chengjie Ma, Junping Du, Yingxia Shao +2
We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can si…
Chinese Word Sense Embedding with SememeWSD and Synonym Set
Yangxi Zhou, Junping Du, Zhe Xue +2
Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if po…