Machine Learning Group

Machine Learning

• Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, and Houqiang Li, Masked Contrastive Representation Learning for Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
• Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu, Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart, (opens in new tab) CVPR 2022.
• Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu, Revisiting Over-Smoothness in Text to Speech (opens in new tab), ACL 2022.
• Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan, ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation, ACL 2022.
• Akiko Eriguchi, Shufang Xie, Hany Hassan, Tao Qin. Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations. NAACL 2022.
  Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo, Yi Ren, Tao Qin, Tie-Yan Liu. A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation. NAACL 2022.
• Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang, Differentially Private Fine-tuning of Language Models (opens in new tab), ICLR 2022.
• Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu, Gradient Information Matters in Policy Optimization by Back-propagating through Model (opens in new tab), ICLR 2022.
• Bohang Zhang, Du Jiang, Di He, Liwei Wang, Boosting the Certified Robustness of L-infinity Distance Nets (opens in new tab), ICLR 2022.
• Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu, DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting (opens in new tab), ICLR 2022.
• Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu, PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior (opens in new tab), ICLR 2022.
• Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama, Exploiting Class Activation Value for Partial-Label Learning (opens in new tab), ICLR 2022.
• Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin, GNN is a Counter? Revisiting GNN for Question Answering (opens in new tab), ICLR 2022.
• Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Rui Yan, Tie-Yan Liu, Target-Side Data Augmentation for Sequence Generation (opens in new tab), ICLR 2022.
• Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu, Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality (opens in new tab), ICLR 2022.
• Jiaxin Shi, Chang Liu, Lester Mackey, Sampling with Mirrored Stein Operators (opens in new tab), ICLR 2022.
• Xiaobo Liang, Lijun Wu, Juntao Li, Tao Qin, Min Zhang, Tie-Yan Liu, Multi-Teacher Distillation with Single Model for Neural Machine Translation (opens in new tab), IEEE/ACM Transactions on Audio, Speech and Language Processing 2022.
• Xinwei Sun, Wu Botong, Zheng Xiangyu, Liu Chang, Wei Chen, Tao Qin, and Tie-Yan Liu, Recovering Latent Causal Factor for Generalization to Distributional Shifts (opens in new tab), NeurIPS 2021.
• Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, and Tie-Yan Liu, Learning causal semantic representation for out-of-distribution prediction (opens in new tab), NeurIPS 2021.
• Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu, R-Drop: Regularized Dropout for Neural Networks (opens in new tab), NeurIPS 2021.
• Jiawei Chen, Xu Tan, Yichong Leng, Jin Xu, Guihua Wen, Tao Qin, Tie-Yan Liu, Speech-T: Transducer for Text to Speech and Beyond (opens in new tab), NeurIPS 2021.
• Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu, Stable Fast and Accurate: Kernelized Attention with Relative Positional Encoding (opens in new tab), NeurIPS 2021.
• Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu, Do Transformers Really Perform Bad for Graph Representation? (opens in new tab), NeurIPS 2021.
• Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu, Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (opens in new tab), NeurIPS 2021.
• Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu, Curriculum Offline Imitating Learning (opens in new tab), NeurIPS 2021.
• Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu, Distributional Reinforcement Learning for Multi-Dimensional Reward Functions (opens in new tab), NeurIPS 2021.
• Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu, On the Generative Utility of Cyclic Conditionals (opens in new tab), NeurIPS 2021.
• Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu, Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD (opens in new tab), NeurIPS 2021.
• Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiangyang Li, Edward Lin, Tie-Yan Liu, FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition (opens in new tab), NeurIPS 2021.
• Jinpeng Li, Yingce Xia, Hongda Sun, Dongyan Zhao, Tie-Yan Liu, Rui Yan, Stylized Dialogue Generation with Multi-Pass Dual Learning (opens in new tab), NeurIPS 2021.
• Yuzi Yan, Xu Tan, Bohan Li, Guangyan Zhang, Tao Qin, Sheng Zhao, Yuan Shen, Wei-Qiang Zhang, Tie-Yan Liu, AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style (opens in new tab), INTERSPEECH 2021.
• Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu, On the Implicit Regularization for Adaptive Optimization Algorithms on Homogeneous Neural Netw/borks (opens in new tab), ICML 2021.
• Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu, Large Scale Private Learning via Low-rank Reparametrization (opens in new tab), ICML 2021.
• Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu, Temporally Correlated Task Scheduling for Sequence Learning (opens in new tab), ICML 2021.
• Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang, GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training (opens in new tab), ICML 2021.
• Dinglan Peng, Shuxin Zheng, Yatao Liu, Guolin Ke, Di He, and Tie-Yan Liu, How could Neural Networks understand Programs (opens in new tab), ICML 2021.
• Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk, Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder (opens in new tab), EMNLP 2021.
• Jin Xu, Xu Tan, Renqian Luo, Kaitao Song, Jian Li, Tao Qin, Tie-Yan Liu, NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search (opens in new tab), KDD 2021.
• Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, and Tie-Yan Liu, Path-BN: Towards Effective Batch Normalization in the Path Space for ReLU Networks (opens in new tab), UAI 2021.
• Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, Tie-Yan Liu, MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training (opens in new tab), ACL 2021.
• Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu, DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (opens in new tab), ACL 2021.
• Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, Tie-Yan Liu, Independence-aware Advantage Estimation (opens in new tab), IJCAI 2021.
• Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, Tie-Yan Liu, MFVFD: Mean-Field based Individual Value Function Decomposition Multi-Agent Q-Learning for Stochastic Games (opens in new tab), IJCAI 2021.
• Rui Wang, Xu Tan, Renqian Luo, Tao Qin, and Tie-Yan Liu, A Survey on Low-Resource Neural Machine Translation (opens in new tab), IJCAI 2021.
• Zhen Wu, Lijun Wu, Qi Meng, Yingce Xia, Shufang Xie, Tao Qin, Xinyu Dai, and Tie-Yan Liu, UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (opens in new tab), NAACL 2021.
• Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu, AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data (opens in new tab), ICASSP 2021.
• Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Jinzhu Li, Sheng Zhao, Enhong Chen, Tie-Yan Liu, LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search (opens in new tab), ICASSP 2021.
• Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu, DenoiSpeech: Denoising Text to Speech with Frame-Level Noise Modeling (opens in new tab), ICASSP 2021.
• Yichong Leng, Xu Tan, Sheng Zhao, Frank Soong, Xiang-Yang Li, Tao Qin, MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network (opens in new tab), ICASSP 2021.
• Da Yu, Huishuai Zhang, Wei Chen and Tie-Yan Liu, Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning (opens in new tab), ICLR 2021.
• Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu, Return-Based Contrastive Representation Learning for Reinforcement Learning (opens in new tab), ICLR 2021.
• Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng Zhao, Tie-Yan Liu, AdaSpeech: Adaptive Text to Speech for Custom Voice (opens in new tab), ICLR 2021.
• Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu, FastSpeech 2: Fast and High-Quality End-to-End Text to Speech (opens in new tab), ICLR 2021.
• Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu, Taking Notes on the Fly Helps Language Pre-training (opens in new tab), ICLR 2021.
• Guolin Ke, Di He, Tie-Yan Liu, Rethinking Positional Encoding in Language Pre-training (opens in new tab), ICLR 2021.
• Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, IOT: Instance-wise Layer Reordering for Transformer Structures (opens in new tab), ICLR 2021.
• Wentao Xu, Chang Xu, Weiqing Liu, Jiang Bian, and Tie-Yan Liu, REST: Relational Event-driven Stock Trend Forecasting (opens in new tab), WebConf 2021.
• Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian and Tie-Yan Liu, Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representation (opens in new tab), AAMAS 2021.
• Da Yu, Huishuai Zhang, Wei Chen, Jian Yin and Tie-Yan Liu, How Does Data Augmentation Affect Privacy in Machine Learning? (opens in new tab), AAAI 2021.
• Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, Tao Qin, SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint (opens in new tab), AAAI 2021.
• Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Xiangyang Li, Tao Qin, Learning to Reweight with Deep Interactions (opens in new tab), AAAI 2021.
• Chen Zhang, Xu Tan, Yi Ren, Tao Qin, Kejun Zhang, Tie-Yan Liu, UWSpeech: Speech to Speech Translation for Unwritten Languages (opens in new tab), AAAI 2021.
• Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, and Tao Qin, mixSeq: A Simple Data Augmentation Method for Neural Machine Translation (opens in new tab), IWSLT 2021.
• Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, and Tie-Yan Liu, Convergence Theory of Learning Over-parameterized ResNet: A Full Characterization (opens in new tab), Machine Learning Journal 2021.
• Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu, Demonstration actor critic, (opens in new tab) Neurocomputing 2021.
• Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu, Semi-Supervised Neural Architecture Search (opens in new tab), NeurIPS 2020.
• Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MPNet: Masked and Permuted Pre-training for Language Understanding (opens in new tab), NeurIPS 2020.
• Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu, RD^2: Reward Decomposition with Representation Decomposition (opens in new tab), NeurIPS 2020.
• Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu, PopMAG: Pop Music Accompaniment Generation (opens in new tab), ACM Multimedia 2020.
• Weicong Chen, Xu Tan, Yingce Xia, Tao Qin, Yu Wang, and Tie-Yan Liu, DualLip: A System for Joint Lip Reading and Generation (opens in new tab), ACM Multimedia 2020.
• Peiling Lu, Jie Wu, Jian Luan, Xu Tan, Li Zhou, XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System (opens in new tab), INTERSPEECH 2020.
• Mingjian Chen, Xu Tan, Yi Ren, Jin Xu, Hao Sun, Sheng Zhao, Tao Qin, Tie-Yan Liu, MultiSpeech: Multi-Speaker Text to Speech with Transformer (opens in new tab), INTERSPEECH 2020.
• Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu, On Layer Normalization in the Transformer Architecture (opens in new tab), ICML 2020.
• Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Tao Qin, Jian-Huang Lai, and Tie-Yan Liu, Sequence Generation with Mixed Representations (opens in new tab), ICML 2020.
• Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, and Tie-Yan Liu, Invertible Image Rescaling (opens in new tab), ECCV 2020.
• Yi Ren, Xu Tan, Tao Qin, Jian Luan, Zhou Zhao, Tie-Yan Liu, DeepSinger: Singing Voice Synthesis with Data Mined from the Web (opens in new tab), KDD 2020.
• Jin Xu, Xu Tan, Yi Ren, Tao Qin, Jian Li, Sheng Zhao, Tie-Yan Liu, LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition (opens in new tab), KDD 2020.
• Da Yu, Huishuai Zhang, Wei Chen, Jian Yin and Tie-Yan Liu, Gradient Perturbation is Underrated for Differentially Private Convex Optimization (opens in new tab), IJCAI 2020.
• Jinglin Liu, Yi Ren, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu, Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation (opens in new tab), IJCAI 2020.
• Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, and Tie-Yan Liu, SEEK: Segmented Embedding of Knowledge Graph (opens in new tab), ACL 2020.
• Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao QIN, Zhou Zhao and Tie-Yan Liu, SimulSpeech: End-to-End Simultaneous Speech to Text Translation, (opens in new tab) ACL 2020.
• Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao and Tie-Yan Liu, A Study of Non-autoregressive Model for Sequence Generation (opens in new tab), ACL 2020.
• Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie, Leveraging demonstrations for reinforcement recommendation reasoning over knowledge graphs (opens in new tab), SIGIR 2020.
• Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue, Takenori Yoshimura, Shinji Watanabe, Tomoki Toda, Kazuya Takeda, Yu Zhang, Xu Tan, ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit (opens in new tab), ICASSP 2020.
• Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, Incorporating BERT into Neural Machine Translation (opens in new tab), ICLR 2020.
• Yiren Wang, Lijun Wu, Yingce Xia, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu, Transductive Ensemble Learning for Neural Machine Translation (opens in new tab), AAAI 2020.
• Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Tie-Yan Liu, Enhong Chen, Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation (opens in new tab), AAAI 2020.
• Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, and Tie-Yan Liu, Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data (opens in new tab), IEEE Transactions on Signal Processing 2020.
• Yang Fan, Fei Tian, Yingce Xia, Tao Qin, Xiangyang Li, and Tie-Yan Liu, Searching Better Architectures for Neural Machine Translation (opens in new tab), IEEE/ACM Transactions on Audio, Speech and Language Processing 2020.
• Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu, Neural Machine Translation with Soft Prototype (opens in new tab), NeurIPS 2019.
• Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu, FastSpeech: Fast, Robust and Controllable Text to Speech (opens in new tab), NeurIPS 2019.
• Derek Yang, Li Zhao, Zichuan Lin, Jiang Bian, Tao Qin, and Tie-Yan Liu, Fully Parameterized Quantile Function for Distributional Reinforcement Learning (opens in new tab), NeurIPS 2019.
• Zichuan Lin, Li Zhao,  Derek Yang, Tao Qin, Guangwen Yang, and Tie-Yan Liu, Distributional Reward Decomposition for Reinforcement Learning (opens in new tab),  NeurIPS 2019.
• Lu Hou, Jinhua Zhu, James Tin-Yau Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu, Normalization Helps Training of Quantized LSTM (opens in new tab), NeurIPS 2019.
• Hao Sun, Xu Tan, Jun-Wei Gan, Sheng Zhao, Dongxu Han, Hongzhi Liu, Tao Qin, and Tie-Yan Liu, Knowledge Distillation from BERT in Pre-training and Fine-tuning for Polyphone Disambiguation (opens in new tab), ASRU 2019.
• Yi Zhou, Huishuai Zhang and Yingbin Liang, Understanding Generalization Error of SGD in Nonconvex Optimization (opens in new tab), ICASSP 2019.
• Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu, Sheng Zhao, Tao Qin, and Tie-Yan Liu, Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion (opens in new tab), InterSpeech 2019.
• Lijun Wu, Yiren Wang, Yingce Xia, Tao Qin, Jianwen Lai, and Tie-Yan Liu, Exploiting Monolingual Data at Scale for Neural Machine Translation (opens in new tab), EMNLP 2019.
• Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao QIN, and Tie-Yan Liu, Multilingual Neural Machine Translation with Language Clustering (opens in new tab), EMNLP 2019.
• Lijun Wu, Jinhua Zhu, Fei Gao, Di He, Tao QIN, Jianhuang Lai, and Tie-Yan Liu, Machine Translation With Weakly Paired Documents (opens in new tab), EMNLP 2019.
• Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao QIN, Liwei WANG, and Tie-Yan Liu, Hint-based Training for Non-AutoRegressive Machine Translation (opens in new tab), EMNLP 2019.
• Yingce Xia, Xu Tan, et al. Microsoft Research Asia’s Systems for WMT19 (opens in new tab), WMT 2019.
• Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, and Tie-Yan Liu, Soft Contextual Data Augmentation for Neural Machine Translation (opens in new tab), ACL 2019.
• Yichong Leng, Xu Tan, Tao QIN, Xiang-Yang Li and Tie-Yan Liu, Unsupervised Pivot Translation for Distant Languages (opens in new tab), ACL 2019.
• Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao QIN, and Tie-Yan Liu, Depth Growing for Neural Machine Translation (opens in new tab), ACL 2019.
• Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu, Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models (opens in new tab), IJCAI 2019.
• Mingyang Yi, Huishuai Zhang, Wei Chen, Zhiming Ma and Tie-Yan Liu, BN-invariant Sharpness Regularizes the Training Model to Better Generalization (opens in new tab), IJCAI 2019.
• Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian and Tie-Yan Liu, DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (opens in new tab), KDD 2019.
• Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation (opens in new tab), ICML 2019.
• Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Sheng Zhao, Tie-Yan Liu, Almost Unsupervised Text to Speech and Automatic Speech Recognition (opens in new tab), ICML 2019.
• Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu, Efficient Training of BERT by Progressively Stacking (opens in new tab), ICML 2019.
• Lijun Zhang, Tie-Yan Liu, and Zhi-Hua Zhou, Adaptive Regret of Convex and Smooth Functions (opens in new tab), ICML 2019.
• Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, and Tie-Yan Liu, A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network (opens in new tab), AAMAS 2019.
• Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, Representation Degeneration Problem in Training Natural Language Generation Models (opens in new tab), ICLR 2019.
• Qi Meng*, Shuxin Zheng*, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space (opens in new tab), ICLR 2019.
• Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang and Vahid Tarokh, SGD Converges to Global Minimum in Deep Learning via Star-convex Path (opens in new tab), ICLR 2019.
• 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.
• Xu Tan, Yi Ren, Di He, Tao Qin, Tie-Yan Liu, Multilingual Neural Machine Translation with Knowledge Distillation (opens in new tab), ICLR 2019.
• Yiren Wang, Fei Tian, Di He, Tao Qin, Chengxiang Zhai, Tie-Yan Liu, Non-Autoregressive Machine Translation with Auxiliary Regularization (opens in new tab), AAAI 2019.
• Junliang Guo, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu, Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input (opens in new tab), AAAI 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.
• Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin, Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder (opens in new tab), AAAI 2019.
• Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang and Tie-Yan Liu, Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks (opens in new tab), AAAI 2019.
• Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, and Tie-Yan Liu, Capacity Control of ReLU Neural Networks by Basis-path Norm, (opens in new tab) AAAI 2019.
• Wenzheng Hu, Junqi Jin, Tie-Yan Liu, and Changshui Zhang, Automatically Design Convolutional Neural Networks by Optimization with Submodularity and Supermodularity (opens in new tab), IEEE Transactions on Neural Networks and Learning Systems 2019.
• Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai, Tie-Yan Liu, Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation (opens in new tab), IEEE Transactions on Audio, Speech and Language Processing 2019.
• Li He, Shuxin Zheng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu, OptQuant: Distributed Training of Neural Networks with Optimized Quantization Mechanisms (opens in new tab), NeuroComputing 2019.
• Huishuai Zhang, Wei Chen and Tie-Yan Liu, On the local Hessian in back-propagation (opens in new tab), NeurIPS 2018.
• Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation (opens in new tab), NIPS 2018.
• Lijun Wu, Fei Tian, Yingce Xia, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Learning to Teach with Dynamic Loss Functions (opens in new tab), NeurIPS 2018.
• Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu, Neural Architecture Optimization (opens in new tab), NIPS 2018.
• Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu, FRAGE: Frequency-Agnostic Word Representations (opens in new tab), NIPS 2018.
• Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation (opens in new tab), EMNLP 2018.
• Lijun Wu, Tan Xu, Di He, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (opens in new tab), EMNLP 2018.
• Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Adversarial Neural Machine Translation (opens in new tab), ACML 2018.
• Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning (opens in new tab), ICML 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.
• Chenyan Xiong, Zhengzhong Liu, Jamie Callan and Tie-Yan Liu, Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling (opens in new tab), SIGIR 2018.
• Li Han, Qi Meng, Wei Chen, Zhiming Ma, Tie-Yan Liu, Differential Equations for Modeling Asynchronous Algorithms (opens in new tab), IJCAI 2018.
• Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Conditional Image-to-Image Translation (opens in new tab), CVPR 2018.
• Fei Tian, Tao Qin, and Tie-Yan Liu, Learning to Teach (opens in new tab), ICLR 2018.
• 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.
• Yanyao Shen, Xu Tan, Di He, Tao QIN, and Tie-Yan Liu, Dense Information Flow for Neural Machine Translation (opens in new tab), NAACL 2018.
• Shizhao Sun, Wei Chen, Jiang Bian, Tie-Yan Liu, Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning (opens in new tab), AAMAS 2018.
• Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, and Tie-Yan Liu, Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization (opens in new tab), AAAI 2018.
• Lijun Wu, Fei Tian, Li Zhao, Jianhuang Lai and Tie-Yan Liu, Word Attention for Sequence to Sequence Text Understanding (opens in new tab), AAAI 2018.
• Fei Tian, Tao Qin, and Tie-Yan Liu, Computational Pricing in Internet Era (opens in new tab), Frontiers of Computer Science 2018.
• Liang He, Bin Shao, Yanghua Xiao, Yatao Li, Tie-Yan Liu, Enhong Chen, and Huanhuan Xia, Neurally-Guided Semantic Navigation in Knowledge Graph (opens in new tab), IEEE Transactions on Big Data 2018.
• Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, Ming Zhou, Achieving Human Parity on Automatic Chinese to English News Translation (opens in new tab), arXiv preprint arXiv:1803.05567, 2018.
• Wang Yue, Chen Wei, Liu Yuting, Ma Zhi-Ming, Liu Tie-Yan, Finite sample analysis of the GTD policy evaluation algorithms in Markov setting (opens in new tab), NeurIPS 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.
• Yue Wang, Wei Chen, Yuting Liu, and Tie-Yan Liu, Finite Sample Analysis of GTD Policy Evaluation Algorithms in Markov Setting (opens in new tab), NIPS 2017.
• Guolin Ke, Qi Meng, Taifeng Wang, Wei Chen, Weidong Ma, Tie-Yan Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree (opens in new tab), NIPS 2017.
• Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu and Tie-Yan Liu, Dual Supervised Learning (opens in new tab), ICML 2017.
• Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, and Tie-Yan Liu, Asynchronous Stochastic Gradient Descent with Delay Compensation (opens in new tab), ICML 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, Tao Qin, Nenghai Yu and Tie-Yan Liu, Sequence Generation with Target Attention (opens in new tab), ECML 2017.
• Shizhao Sun, Wei Chen, Jiang Bian, and Tie-Yan Liu, Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks (opens in new tab), ECML 2017.
• Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction (opens in new tab), AAAI 2017.
• Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu, Generalization Error Bounds for Optimization Algorithms via Stability (opens in new tab), AAAI 2017.
• Jiang Rong, Tao Qin, Bo An and Tie-Yan Liu, Revenue Maximization for Finitely Repeated Ad Auctions (opens in new tab), AAAI 2017.
• Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun and Tie-Yan Liu, Randomized Mechanisms for Selling Reserved Instances in Cloud Computing (opens in new tab), AAAI 2017.
• 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.
• Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu, A Communication-efficient Parallel Algorithm for Decision Tree (opens in new tab), NIPS 2016.
• Huazheng Wang, Fei Tian, Bin Gao, Chenjieren Zhu, Jiang Bian, Tie-Yan Liu, Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding (opens in new tab), EMNLP 2016.
• Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu and Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays (opens in new tab), IJCAI 2016.
• Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, (opens in new tab) IJCAI 2016.
• Yingce Xia, Tao Qin, Tie-Yan Liu, Best Action Selection in a Stochastic Environment (opens in new tab), AAMAS 2016.
• Tie-Yan Liu, Weidong Ma, Pingzhong Tang, Tao Qin, Guang Yang, Bo Zheng, Online Non-Preemptive Story Scheduling in Web Advertising (opens in new tab), AAMAS 2016.
• Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Optimal Sample Size for Adword Auctions (opens in new tab), AAMAS 2016.
• Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View (opens in new tab), AAAI 2016.
• Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen, Ranking-oriented Collaborative Filtering: A Listwise Approach, (opens in new tab) ACM Transactions on Information Systems 2016
• Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, and Bo Zheng, Efficient Mechanism Design for Online Scheduling (opens in new tab), Journal of Artificial Intelligence Research 2016.
• Chang Xu, Gang Wang, Xiaoguang Liu, Tie-Yan Liu, Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks (opens in new tab), IEEE Transactions on Computers 2016.
• Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search (opens in new tab), Applied Stochastic Models in Business and Industry 2016.
• Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Thompson Sampling for Budgeted Multi-armed Bandits (opens in new tab), IJCAI 2015.
• Bolei Xu, Tao Qin, Guoping Qiu, and Tie-Yan Liu, Competitive Pricing for Cloud Computing in an Evolutionary Market (opens in new tab), IJCAI 2015.
• Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, and Tie-Yan Liu, Selling Reserved Instances in Cloud Computing (opens in new tab), IJCAI 2015.
• Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, and Jari Veijalainen, Listwise Collaborative Filtering (opens in new tab), SIGIR 2015
• Binyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals (opens in new tab), AAMAS 2015.
• Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Xing, Tie-Yan Liu, and Wei-Ying Ma, LightLDA: Big Topic Models on Modest Computer Cluster (opens in new tab), WWW 2015.
• Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data (opens in new tab), AAAI 2015.
• Haifang Li, Wei Chen, Fei Tian, Tao Qin, and Tie-Yan Liu, Generalization Analysis for Game-theoretic Machine Learning, (opens in new tab) AAAI 2015.
• Qing Cui, Bin Gao, Jiang Bian, Hanjun Dai, and Tie-Yan Liu, KNET: A General Framework for Learning Word Embedding using Morphological Knowledge (opens in new tab), ACM Transactions on Information Systems 2015.
• Wei Wei, Bin Gao, Tie-Yan Liu, Taifeng Wang, Guohui Li, and Hang Li, A Ranking Approach on Large-scale Graph with Multi-dimensional Heterogeneous Information, (opens in new tab) IEEE Transactions on Cybernetics 2015.

AI for Industry

• Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian, DDG-DA: Data Distribution Generation for Predictable ConceptDrift Adaptation, (opens in new tab) AAAI 2022.
• Yang Fan, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Tao Qin, Back Translation for Molecule Generation (opens in new tab), Bioinformatics 2021.
• Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian, Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport (opens in new tab), KDD 2021.
• Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu, Temporally Correlated Task Scheduling for Sequence Learning (opens in new tab), ICML 2021.
• Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu, REST: Relational Event-driven Stock Trend Forecasting (opens in new tab), WWW 2021.
• Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu, Universal Trading for Order Execution with Oracle Policy Distillation (opens in new tab), AAAI 2021.
• Shun Zheng, Zhifeng Gao, Wei Cao, Jiang Bian, Tie-Yan Liu, HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting (opens in new tab), CIKM 2021.
• Bo Yang, Lijun Wu*, How to Leverage Multimodal EHR Data for Better Medical Predictions? (opens in new tab), EMNLP-2021.
• Boning Li, Yingce Xia, Shufang Xie, Lijun Wu and Tao Qin, Distance-Enhanced Graph Neural Network for Link Prediction, in the 2021 ICML Workshop on Computational Biology, (opens in new tab) ICML-WCB 2021.
• Chi Chen, Li Zhao, Jiang Bian, Chunxiao Xing, Tie-Yan Liu, Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction (opens in new tab), KDD 2019.
• Zhige Li, Derek yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu, Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding (opens in new tab), KDD 2019.
• Xiao Yang, Weiqing Liu, Lewen Wang, Cheng Qu, Jiang Bian, A divide-and-conquer framework for attention-based combination of multiple investment strategies (opens in new tab), IEEE, GlobalSIP 2019.
• Lewen Wang, Weiqing Liu, Xiao Yang, Jiang Bian, Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction (opens in new tab), IEEE, GlobalSIP 2019.
• Yi Ding, Weiqing Liu, Jiang Bian, Daoqiang Zhang, Tie-Yan Liu, Investor-imitator: A framework for trading knowledge extraction (opens in new tab), KDD 2018.
• Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan, Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction (opens in new tab), WSDM 2018.

AI for Science

• Siyuan Liu, Yusong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu, Tong Wang, Improved drug–target interaction prediction with intermolecular graph transformer (opens in new tab), Briefings in Bioinformatics, 2022.
• Xinquan Wang, Jun Lan, Xinheng He, Yifei Ren, Ziyi Wang, Huan Zhou, Shilong Fan, Chenyou Zhu, Dongsheng Liu, Bin Shao, Tie-Yan Liu, Qisheng Wang, Linqi Zhang, Jiwan Ge, and Tong Wang, Structural insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction (opens in new tab), Cell Research, 2022.
• Yang Fan, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Tao Qin, Back Translation for Molecule Generation (opens in new tab), Bioinformatics 2021.
• Jia Xing, Shuxin Zheng, Siwei Li, Lin Huang, Xiaochun Wang, James T. Kelly, Shuxiao Wang, Chang Liu, Carey Jang, Yun Zhu, Jia Zhang, Jiang Bian, Tie-Yan Liu, Jiming Hao, Mimicking Atmospheric Photochemical Modeling with a Deep Neural Network (opens in new tab), Atmospheric Research 2021.
• Yao Li, Tong Wang, Juanrong Zhang, Bin Shao, Haipeng Gong, Yusong Wang, Xinheng He, Siyuan Liu and Tie-Yan Liu, Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation (opens in new tab), Advanced Theory and Simulation 2021.
• Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu, Machine-Learning Non-Conservative Dynamics for New-Physics Detection (opens in new tab), Physical Review E 2021.
• He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu, Co-evolution Transformer for Protein Contact Prediction (opens in new tab), NeurIPS 2021.
• Siyuan Liu, Tong Wang, Qijiang Xu, Bin Shao, Jian Yin, Tie-Yan Liu, Complementing Sequence-derived Features with Structural Information Extracted from Fragment Libraries for Protein Structure Prediction (opens in new tab), BMC Bioinformatics 2021.
• Wenze Ding, Qijiang Xu, Siyuan Liu, Tong Wang, Bin Shao, Haipeng Gong, Tie-Yan Liu, SAMF: a Self-adaptive Protein Modeling Framework, (opens in new tab) Bioinformatics 2021.
• Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie-Yan Liu, Wei-Mou Zheng, Dongbo Bu, CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (opens in new tab), Nature Communications 2021.
• Jia Xing, Shuxin Zheng, Dian Ding, James T. Kelly, Shuxiao Wang, Siwei Li, Tao Qin, Mingyuan Ma, Zhaoxin Dong, Carey Jang, Yun Zhu, Haotian Zheng, Lu Ren, Tie-Yan Liu, and Jiming Hao, Deep Learning for Prediction of the Air Quality Response to Emission Changes (opens in new tab), Environmental Science & Technology 2020.