Jianshu Chen

is this you? claim profile

Currently a principal researcher at Tencent AI Lab, working on machine learning and natural language processing. Worked in the Deep Learning Technology Center (DLTC) at Microsoft Research, Redmond, WA. Completed my PhD in Electrical Engineering at University of California, Los Angeles (UCLA).

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching

We consider the problem of training speech recognition systems without u...
12/23/2018 ∙ by Chih-Kuan Yeh, et al. ∙ 6 ∙ share

read it
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation

Many important classification problems, such as object classification, s...
06/06/2019 ∙ by Yu Liu, et al. ∙ 3 ∙ share

read it
A Learning-to-Infer Method for Real-Time Power Grid Topology Identification

Identifying arbitrary topologies of power networks in real time is a com...
10/21/2017 ∙ by Yue Zhao, et al. ∙ 0 ∙ share

read it
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

We present an architecture of a recurrent neural network (RNN) with a fu...
11/24/2013 ∙ by Jianshu Chen, et al. ∙ 0 ∙ share

read it
Stochastic Variance Reduction Methods for Policy Evaluation

Policy evaluation is a crucial step in many reinforcement-learning proce...
02/25/2017 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

read it
Recurrent Reinforcement Learning: A Hybrid Approach

Successful applications of reinforcement learning in real-world problems...
09/10/2015 ∙ by Xiujun Li, et al. ∙ 0 ∙ share

read it
Distributed Policy Evaluation Under Multiple Behavior Strategies

We apply diffusion strategies to develop a fully-distributed cooperative...
12/30/2013 ∙ by Sergio Valcarcel Macua, et al. ∙ 0 ∙ share

read it
Character-level Deep Conflation for Business Data Analytics

Connecting different text attributes associated with the same entity (co...
02/08/2017 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

read it
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

We introduce an online popularity prediction and tracking task as a benc...
06/12/2016 ∙ by Ji He, et al. ∙ 0 ∙ share

read it
Deep Reinforcement Learning with a Natural Language Action Space

This paper introduces a novel architecture for reinforcement learning wi...
11/14/2015 ∙ by Ji He, et al. ∙ 0 ∙ share

read it
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval

This paper develops a model that addresses sentence embedding, a hot top...
02/24/2015 ∙ by Hamid Palangi, et al. ∙ 0 ∙ share

read it
Smoothed Dual Embedding Control

We revisit the Bellman optimality equation with Nesterov's smoothing tec...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

read it
ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search

Learning to walk over a graph towards a target node for a given input qu...
02/12/2018 ∙ by Yelong Shen, et al. ∙ 0 ∙ share

read it
XL-NBT: A Cross-lingual Neural Belief Tracking Framework

Task-oriented dialog systems are becoming pervasive, and many companies ...
08/19/2018 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

read it
P-MCGS: Parallel Monte Carlo Acyclic Graph Search

Recently, there have been great interests in Monte Carlo Tree Search (MC...
10/28/2018 ∙ by Chen Yu, et al. ∙ 0 ∙ share

read it
Incorporating Structured Commonsense Knowledge in Story Completion

The ability to select an appropriate story ending is the first step towa...
11/01/2018 ∙ by Jiaao Chen, et al. ∙ 0 ∙ share

read it
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

We present DREAM, the first dialogue-based multiple-choice reading compr...
02/01/2019 ∙ by Kai Sun, et al. ∙ 0 ∙ share

read it
Evidence Sentence Extraction for Machine Reading Comprehension

Recently remarkable success has been achieved in machine reading compreh...
02/23/2019 ∙ by Hai Wang, et al. ∙ 0 ∙ share

read it
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention

Semantically controlled neural response generation on limited-domain has...
05/30/2019 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

read it
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

We consider a generic empirical composition optimization problem, where ...
07/22/2019 ∙ by Adithya M. Devraj, et al. ∙ 0 ∙ share

read it
TabFact: A Large-scale Dataset for Table-based Fact Verification

The problem of verifying whether a textual hypothesis holds the truth ba...
09/05/2019 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

read it
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach

Recently, pretrained language models (e.g., BERT) have achieved great su...
09/20/2019 ∙ by Shiyang Li, et al. ∙ 0 ∙ share

read it

Jianshu Chen

Featured Co-authors

Xin Wang

H. Vincent Poor

Jianfeng Gao

William Yang Wang

Le Song

Ji Liu

Xiaodong He

Yu Liu

Dan Roth

Dong Yu

Lihong Li

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching

From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation

A Learning-to-Infer Method for Real-Time Power Grid Topology Identification

A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

Stochastic Variance Reduction Methods for Policy Evaluation

Recurrent Reinforcement Learning: A Hybrid Approach

Distributed Policy Evaluation Under Multiple Behavior Strategies

Character-level Deep Conflation for Business Data Analytics

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

Deep Reinforcement Learning with a Natural Language Action Space

Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval

Smoothed Dual Embedding Control

ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

P-MCGS: Parallel Monte Carlo Acyclic Graph Search

Incorporating Structured Commonsense Knowledge in Story Completion

DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

Evidence Sentence Extraction for Machine Reading Comprehension

Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention

Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

TabFact: A Large-scale Dataset for Table-based Fact Verification

Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach