no code implementations • MIDL 2019 • Shilin Chen, Ji He, Jianhua Ma
The presented DL framework is denoted as DL-ME for simplicity.
no code implementations • IEEE Transactions on Geoscience and Remote Sensing 2019 • Ji He, Lina Zhao, HongWei Yang, Mengmeng Zhang, Wei Li
Moreover, several attentions are learned by different heads, and each head of the MHSA layer encodes the semantic context-aware representation to obtain discriminative features.
no code implementations • 9 Aug 2018 • Ji He, Jianhua Ma
Qualitative results show promising reconstruction performance of the iRadonMap.
no code implementations • NeurIPS 2017 • Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.
no code implementations • 20 Apr 2017 • Ji He, Mari Ostendorf, Xiaodong He
This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces.
1 code implementation • EMNLP 2016 • Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.
3 code implementations • ACL 2016 • Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
no code implementations • 10 Sep 2015 • Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, Ji He
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.
1 code implementation • NeurIPS 2015 • Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.