Search Results for author: Yuanshao Zhu

Found 7 papers, 3 papers with code

Large Language Model Distilling Medication Recommendation Model

1 code implementation5 Feb 2024 Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, Yefeng Zheng

In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).

Knowledge Distillation Language Modelling +2

Enhancing Traffic Prediction with Learnable Filter Module

no code implementations24 Oct 2023 Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, James J. Q. Yu

Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.

Traffic Prediction

MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications

1 code implementation21 Oct 2023 Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng

Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks.

Multi-Task Learning

Traffic Prediction with Transfer Learning: A Mutual Information-based Approach

no code implementations13 Mar 2023 Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang, James J. Q. Yu

In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic.

Graph Reconstruction Management +2

AutoDenoise: Automatic Data Instance Denoising for Recommendations

no code implementations12 Mar 2023 Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang

In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.

Denoising Recommendation Systems

Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis

no code implementations14 Oct 2021 Yi Liu, Yuanshao Zhu, James J. Q. Yu

Similarly, due to the heterogeneity of the connected remote devices, FEEL faces the challenge of heterogeneous data and non-IID (Independent and Identically Distributed) data.

Binary Classification Ensemble Learning +1

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