Y. Shen

Publications
Influence

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

Y. Shen, X. He, Jianfeng Gao, L. Deng, G. Mesnil
Computer Science
CIKM
3 November 2014

We propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents. Expand

Learning semantic representations using convolutional neural networks for web search

Y. Shen, X. He, Jianfeng Gao, Li Deng, G. Mesnil
Computer Science
WWW
7 April 2014

This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. Expand

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

H. Palangi, L. Deng, +5 authors R. Ward
Computer Science
IEEE/ACM Transactions on Audio, Speech, and…
24 February 2015

This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. Expand

ReasoNet: Learning to Stop Reading in Machine Comprehension

Y. Shen, Po-Sen Huang, Jianfeng Gao, W. Chen
Computer Science
CoCo@NIPS
17 September 2016

ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Expand

Stochastic Answer Networks for Machine Reading Comprehension

X. Liu, Y. Shen, Kevin Duh, Jianfeng Gao
Computer Science
ACL
10 December 2017

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Expand

FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension

H. Huang, Chenguang Zhu, Y. Shen, W. Chen
Computer Science
ICLR
16 November 2017

This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. Expand

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

Jianshu Chen, Ji He, +5 authors L. Deng
Computer Science, Mathematics
NIPS
14 August 2015

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. Expand

M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search

Y. Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao
Computer Science
NeurIPS
1 February 2018

We develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). Expand

Language-Based Image Editing with Recurrent Attentive Models

J. Chen, Y. Shen, Jianfeng Gao, J. Liu, X. Liu
Computer Science
IEEE/CVF Conference on Computer Vision and…
16 November 2017

We investigate the problem of Language-Based Image Editing (LBIE). Given a source image and a natural language description, we want to generate a target image by editing the source image based on the… Expand

Semantic Modelling with Long-Short-Term Memory for Information Retrieval

H. Palangi, L. Deng, +5 authors R. Ward
Computer Science
ArXiv
19 December 2014

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods… Expand

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