Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue

no code implementations • 20 Jun 2024 • Huifang Du, Shuqin Li, Minghao Wu, Xuejing Feng, Yuan-Fang Li, Haofen Wang

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems.

Dialogue State Tracking Reinforcement Learning (RL) +1

A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images

no code implementations • 5 May 2022 • Tingting Zheng, Weixing Chen, Shuqin Li, Hao Quan, Qun Bai, Tianhang Nan, Song Zheng, Xinghua Gao, Yue Zhao, Xiaoyu Cui

Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference.

Deep Reinforcement Learning Knowledge Distillation +3

Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images

no code implementations • 1 Nov 2020 • Fengying Che, Ruichuan Shi, Jian Wu, Haoran Li, Shuqin Li, Weixing Chen, Hao Zhang, Zhi Li, Xiaoyu Cui

The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics.