47 citations · 117 across the 5 of their papers we have counts for
5 papers
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
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…
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Ji He, Mari Ostendorf, Xiaodong He +4
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified numbe…
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He +4
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 fou…
Recurrent Reinforcement Learning: A Hybrid Approach
Xiujun Li, Lihong Li, Jianfeng Gao +4
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and…
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Jianshu Chen, Ji He, Yelong Shen +5
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 pro…