21 citations · 32 across the 7 of their papers we have counts for
7 papers
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
Shujian Jiao, Bingxuan Li, Lei Wang +4
Proteins are essential to life's processes, underpinning evolution and diversity. Advances in sequencing technology have revealed millions of proteins, underscoring the need for so…
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
Zhijie Bao, Wei Chen, Shengze Xiao +6
We propose DISC-MedLLM, a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthca…
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Wei Chen, Cheng Zhong, Jiajie Peng +1
Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studi…
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
Wei Chen, Zhiwei Li, Hongyi Fang +7
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propo…
Curriculum Learning for Vision-and-Language Navigation
Jiwen Zhang, Zhongyu Wei, Jianqing Fan +1
Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions. Previous works ignore the distribution of sample…
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
Cheng Zhong, Kangenbei Liao, Wei Chen +5
Motivation: Disease diagnosis oriented dialogue system models the interactive consultation procedure as Markov Decision Process and reinforcement learning algorithms are used to so…
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Tianpei Yang, Jianye Hao, Zhaopeng Meng +8
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing tran…