Deep Learning and Representation Learning

We are working on deep learning. We focus on developing new learning strategies and more efficient algorithms, designing better neural network structures, and improving representation learning.

Efficient Deep Learning
Xiang Li, Tao Qin, Jian Yang, and Tie-Yan Liu, LightRNN: Memory and Computation-Efficient Recurrent Neural Networks (opens in new tab), NIPS 2016. [Code@GitHub (opens in new tab)]
Fei Gao, Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Efficient Sequence Learning with Group Recurrent Networks (opens in new tab), NAACL 2018.
Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, and Tie-Yan Liu, Towards Binary-Valued Gates for Robust LSTM Training (opens in new tab), ICML 2018. [code (opens in new tab)] [Chinese article (opens in new tab)]

Improving Representations
Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tieyan Liu，Representation Degeneration Problem in Training Natural Language Generation Models (opens in new tab), ICLR 2019.
Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, FRAGE: Frequency-Agnostic Word Representation (opens in new tab), NIPS 2018. [code (opens in new tab)]

Advanced Learning Strategies
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma, Dual Learning for Machine Translation (opens in new tab), NIPS 2016.
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu and Tie-Yan Liu, Dual Supervised Learning (opens in new tab), ICML 2017.
Lijun Wu, Li Zhao, Tao Qin, and Tie-Yan Liu, Sequence Prediction with Unlabeled Data by Reward Function Learning (opens in new tab), IJCAI 2017.
Yingce Xia, Jiang Bian, Tao Qin, Tie-Yan Liu, Dual Inference for Machine Learning (opens in new tab), IJCAI 2017.
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, and Tie-Yan Liu, Deliberation Networks: Sequence Generation Beyond One-Pass Decoding (opens in new tab), NIPS 2017.
Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, and Tie-Yan Liu, Decoding with Value Networks for Neural Machine Translation (opens in new tab), NIPS 2017.
Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning (opens in new tab), ICML 2018.
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu, Multi-Agent Dual Learning (opens in new tab), ICLR 2019.
Chengyue Gong, Xu Tan, Di He, and Tao Qin, Sentence-wise Smooth Regularization for Sequence to Sequence Learning (opens in new tab), AAAI 2019.

New Network Structures
Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, and Tie-Yan Liu, Double Path Networks for Sequence to Sequence Learning (opens in new tab), COLING 2018.
Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Conditional Image-to-Image Translation (opens in new tab), CVPR 2018.
Chang Xu, Tao Qin, Yalong Bai, Gang Wang and Tie-Yan Liu, Convolutional Neural Networks for Posed and Spontaneous Expression Recognition, ICME 2017.