Search Results for author: Xiaonan He

Found 5 papers, 2 papers with code

SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model

1 code implementation21 Apr 2023 Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Xin Gao

Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population.

Language Modelling Large Language Model

Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking

no code implementations16 Feb 2023 Jing Xu, Dandan song, Chong Liu, Siu Cheung Hui, Fei Li, Qiang Ju, Xiaonan He, Jian Xie

In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.

Contrastive Learning Dialogue State Tracking +1

A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS

no code implementations5 Dec 2022 Wei Shen, Xiaonan He, Chuheng Zhang, Xuyun Zhang, Jian Xie

Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system.

Spoken Dialogue Systems

Inductive Matrix Completion Using Graph Autoencoder

2 code implementations25 Aug 2021 Wei Shen, Chuheng Zhang, Yun Tian, Liang Zeng, Xiaonan He, Wanchun Dou, Xiaolong Xu

However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).

Matrix Completion Recommendation Systems

Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

no code implementations25 Aug 2020 Wei Shen, Xiaonan He, Chuheng Zhang, Qiang Ni, Wanchun Dou, Yan Wang

Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.

Combinatorial Optimization Fairness +2

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