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ORCIDDi He 0001
Person information
- affiliation: Peking University, School of Intelligence Science and Technology, National Key Laboratory of General Artificial Intelligence, Beijing, China
- affiliation: Microsoft Research Asia, Machine Learning Group, Beijing, China
- affiliation (PhD): Peking University, School of EECS, Key Laboratory of Machine Perception, Beijing, China
Other persons with the same name
- Di He — disambiguation page
- Di He 0002
![[0000-0002-0719-8289]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-0719-8289")
— Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Shanghai Key Laboratory of Navigation and Location-Based Services, Shanghai, China (and 2 more) - Di He 0003 — City University of New York, Graduate Center, New York, NY, USA
- Di He 0004 — Amazon Alexa, Seattle, WA, USA (and 2 more)
- Di He 0005 — University of Science and Technology Beijing, Collaborative Innovation Center of Steel Technology, Beijing, China
- Di He 0006 — Beijing Information Science and Technology University, School of Science, Institute of Applied Mathematics, Beijing, China (and 1 more)
- Di He 0007 — Southeast University, School of Cyber Science and Engineering, Nanjing, China
- Di He 0008 — Tianjin Normal University, Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin, China
- Di He 0009 — Vipshop Holdings Limited, Guangzhou, China
- Di He 0010 — Northwestern Polytechnical University, School of Computer Science, Xi'an, China
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2020 – today
- 2025
[i81]Haojun Yu, Di Dai, Ziwei Zhao, Di He, Han Hu, Liwei Wang:
LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive Classifier. CoRR abs/2501.06862 (2025)- [i80]Guhao Feng, Yihan Geng, Jian Guan, Wei Wu, Liwei Wang, Di He:
Theoretical Benefit and Limitation of Diffusion Language Model. CoRR abs/2502.09622 (2025) - 2024
- [j2]Shuqi Lu, Lin Yao, Xi Chen, Hang Zheng, Di He, Guolin Ke:
3D Molecular Generation via Virtual Dynamics. Trans. Mach. Learn. Res. 2024 (2024) - [c67]Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller:
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. AISTATS 2024: 2278-2286 - [c66]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks. ICLR 2024 - [c65]Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang:
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness. ICLR 2024 - [c64]Mingqing Xiao, Yixin Zhu, Di He, Zhouchen Lin:
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning. ICML 2024 - [c63]Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Liwei Wang, Jingjing Xu, Zhi Zhang, Hongxia Yang, Di He:
Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation. ICML 2024 - [c62]Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang:
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning. ICML 2024 - [c61]Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang:
Do Efficient Transformers Really Save Computation? ICML 2024 - [c60]Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He:
REST: Retrieval-Based Speculative Decoding. NAACL-HLT 2024: 1582-1595 - [c59]Shengjie Luo, Yixian Xu, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang:
Bridging Geometric States via Geometric Diffusion Bridge. NeurIPS 2024 - [c58]Haojun Yu, Di Dai, Ziwei Zhao, Di He, Han Hu, Liwei Wang:
LarvSeg: Exploring Image Classification Data for Large Vocabulary Semantic Segmentation via Category-Wise Attentive Classifier. PRCV (1) 2024: 50-64 - [i79]Qingsi Lai, Lin Yao, Zhifeng Gao, Siyuan Liu, Hongshuai Wang, Shuqi Lu, Di He, Liwei Wang, Cheng Wang, Guolin Ke:
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction. CoRR abs/2401.03862 (2024) - [i78]Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang:
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness. CoRR abs/2401.08514 (2024) - [i77]Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Di He, Jingjing Xu, Zhi Zhang, Hongxia Yang, Liwei Wang:
Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation. CoRR abs/2401.16421 (2024) - [i76]Ruichen Li, Chuwei Wang, Haotian Ye, Di He, Liwei Wang:
DOF: Accelerating High-order Differential Operators with Forward Propagation. CoRR abs/2402.09730 (2024) - [i75]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks. CoRR abs/2402.11984 (2024) - [i74]Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang:
Do Efficient Transformers Really Save Computation? CoRR abs/2402.13934 (2024) - [i73]Weihao Jiang, Guodong Liu, Di He, Kun He:
Boosting Meta-Training with Base Class Information for Few-Shot Learning. CoRR abs/2403.03472 (2024) - [i72]Han Zhong, Guhao Feng, Wei Xiong, Li Zhao, Di He, Jiang Bian, Liwei Wang:
DPO Meets PPO: Reinforced Token Optimization for RLHF. CoRR abs/2404.18922 (2024) - [i71]Mingqing Xiao, Yixin Zhu, Di He, Zhouchen Lin:
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning. CoRR abs/2405.16851 (2024) - [i70]Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang:
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning. CoRR abs/2406.16853 (2024) - [i69]Zhenyu He, Jun Zhang, Shengjie Luo, Jingjing Xu, Zhi Zhang, Di He:
Let the Code LLM Edit Itself When You Edit the Code. CoRR abs/2407.03157 (2024) - [i68]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks. CoRR abs/2407.12516 (2024) - [i67]Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang:
How Numerical Precision Affects Mathematical Reasoning Capabilities of LLMs. CoRR abs/2410.13857 (2024) - [i66]Shengjie Luo, Yixian Xu, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang:
Bridging Geometric States via Geometric Diffusion Bridge. CoRR abs/2410.24220 (2024) - 2023
- [c57]Yibin Wang, Yichen Yang, Di He, Kun He:
Robustness-Aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions. ACL (Findings) 2023: 673-687 - [c56]Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. AISTATS 2023: 2792-2804 - [c55]Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. AISTATS 2023: 3034-3047 - [c54]Haiyang Wang, Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, Liwei Wang:
DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets. CVPR 2023: 13520-13529 - [c53]Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
One Transformer Can Understand Both 2D & 3D Molecular Data. ICLR 2023 - [c52]Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He:
Denoising Masked Autoencoders Help Robust Classification. ICLR 2023 - [c51]Bohang Zhang, Shengjie Luo, Liwei Wang, Di He:
Rethinking the Expressive Power of GNNs via Graph Biconnectivity. ICLR 2023 - [c50]Bohang Zhang, Guhao Feng, Yiheng Du, Di He, Liwei Wang:
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests. ICML 2023: 41019-41077 - [c49]Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, Liwei Wang:
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective. NeurIPS 2023 - [i65]Haiyang Wang, Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, Liwei Wang:
DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets. CoRR abs/2301.06051 (2023) - [i64]Bohang Zhang, Shengjie Luo, Liwei Wang, Di He:
Rethinking the Expressive Power of GNNs via Graph Biconnectivity. CoRR abs/2301.09505 (2023) - [i63]Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller:
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. CoRR abs/2302.01925 (2023) - [i62]Shuqi Lu, Lin Yao, Xi Chen, Hang Zheng, Di He, Guolin Ke:
3D Molecular Generation via Virtual Dynamics. CoRR abs/2302.05847 (2023) - [i61]Bohang Zhang, Guhao Feng, Yiheng Du, Di He, Liwei Wang:
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests. CoRR abs/2302.07090 (2023) - [i60]Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, Guolin Ke:
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+. CoRR abs/2303.16982 (2023) - [i59]Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, Liwei Wang:
Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective. CoRR abs/2305.15408 (2023) - [i58]Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang:
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo. CoRR abs/2307.08214 (2023) - [i57]Pengyun Yue, Hanzhen Zhao, Cong Fang, Di He, Liwei Wang, Zhouchen Lin, Song-Chun Zhu:
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity. CoRR abs/2309.13307 (2023) - [i56]Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He:
REST: Retrieval-Based Speculative Decoding. CoRR abs/2311.08252 (2023) - 2022
- [c48]Zhuocheng Gong, Di He, Yelong Shen, Tie-Yan Liu, Weizhu Chen, Dongyan Zhao, Ji-Rong Wen, Rui Yan:
Finding the Dominant Winning Ticket in Pre-Trained Language Models. ACL (Findings) 2022: 1459-1472 - [c47]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. CVPR 2022: 15202-15212 - [c46]Bohang Zhang, Du Jiang, Di He, Liwei Wang:
Boosting the Certified Robustness of L-infinity Distance Nets. ICLR 2022 - [c45]Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang:
HousE: Knowledge Graph Embedding with Householder Parameterization. ICML 2022: 13209-13224 - [c44]Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. NeurIPS 2022 - [c43]Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? NeurIPS 2022 - [c42]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Training Through Time for Spiking Neural Networks. NeurIPS 2022 - [c41]Bohang Zhang, Du Jiang, Di He, Liwei Wang:
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective. NeurIPS 2022 - [i55]Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang:
HousE: Knowledge Graph Embedding with Householder Parameterization. CoRR abs/2202.07919 (2022) - [i54]Di He, Wenlei Shi, Shanda Li, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. CoRR abs/2202.09340 (2022) - [i53]Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu:
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets. CoRR abs/2203.04810 (2022) - [i52]Payal Bajaj, Chenyan Xiong, Guolin Ke, Xiaodong Liu, Di He, Saurabh Tiwary, Tie-Yan Liu, Paul Bennett, Xia Song, Jianfeng Gao:
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals. CoRR abs/2204.06644 (2022) - [i51]Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. CoRR abs/2205.13401 (2022) - [i50]Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? CoRR abs/2206.02016 (2022) - [i49]Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. CoRR abs/2206.04316 (2022) - [i48]Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Di He, Liwei Wang:
One Transformer Can Understand Both 2D & 3D Molecular Data. CoRR abs/2210.01765 (2022) - [i47]Bohang Zhang, Du Jiang, Di He, Liwei Wang:
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective. CoRR abs/2210.01787 (2022) - [i46]Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin:
Online Training Through Time for Spiking Neural Networks. CoRR abs/2210.04195 (2022) - [i45]Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He:
Denoising Masked AutoEncoders are Certifiable Robust Vision Learners. CoRR abs/2210.06983 (2022) - 2021
- [c40]Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk:
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder. EMNLP (1) 2021: 2780-2791 - [c39]Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu:
Taking Notes on the Fly Helps Language Pre-Training. ICLR 2021 - [c38]Guolin Ke, Di He, Tie-Yan Liu:
Rethinking Positional Encoding in Language Pre-training. ICLR 2021 - [c37]Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang:
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training. ICML 2021: 1204-1215 - [c36]Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu:
How could Neural Networks understand Programs? ICML 2021: 8476-8486 - [c35]Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang:
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons. ICML 2021: 12368-12379 - [c34]Abhishek Das, Muhammed Shuaibi, Aini Palizhati, Siddharth Goyal, Aditya Grover, Adeesh Kolluru, Janice Lan, Ammar Rizvi, Anuroop Sriram, Brandon M. Wood, Devi Parikh, Zachary W. Ulissi, C. Lawrence Zitnick, Guolin Ke, Shuxin Zheng, Yu Shi, Di He, Tie-Yan Liu, Chengxuan Ying, Jiacheng You, Yihan He, Rostislav Grigoriev, Ruslan Lukin, Adel Yarullin, Max Faleev:
The Open Catalyst Challenge 2021: Competition Report. NeurIPS (Competition and Demos) 2021: 29-40 - [c33]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. NeurIPS 2021: 22795-22807 - [c32]Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu:
Do Transformers Really Perform Badly for Graph Representation? NeurIPS 2021: 28877-28888 - [i44]Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang:
Towards Certifying 𝓁∞ Robustness using Neural Networks with 𝓁∞-dist Neurons. CoRR abs/2102.05363 (2021) - [i43]Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, Liwei Wang, Tie-Yan Liu:
Revisiting Language Encoding in Learning Multilingual Representations. CoRR abs/2102.08357 (2021) - [i42]Shuqi Lu, Chenyan Xiong, Di He, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk:
Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder. CoRR abs/2102.09206 (2021) - [i41]Chengxuan Ying, Guolin Ke, Di He, Tie-Yan Liu:
LazyFormer: Self Attention with Lazy Update. CoRR abs/2102.12702 (2021) - [i40]Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio:
Transformers with Competitive Ensembles of Independent Mechanisms. CoRR abs/2103.00336 (2021) - [i39]Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu:
How could Neural Networks understand Programs? CoRR abs/2105.04297 (2021) - [i38]Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu:
Adversarial Training with Rectified Rejection. CoRR abs/2105.14785 (2021) - [i37]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? CoRR abs/2106.05234 (2021) - [i36]Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, Di He:
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track. CoRR abs/2106.08279 (2021) - [i35]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. CoRR abs/2106.12566 (2021) - [i34]Bohang Zhang, Du Jiang, Di He, Liwei Wang:
Boosting the Certified Robustness of L-infinity Distance Nets. CoRR abs/2110.06850 (2021) - [i33]Shanda Li, Xiangning Chen, Di He, Cho-Jui Hsieh:
Can Vision Transformers Perform Convolution? CoRR abs/2111.01353 (2021) - 2020
- [c31]Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu:
Invertible Image Rescaling. ECCV (1) 2020: 126-144 - [c30]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. ICLR 2020 - [c29]Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu:
Incorporating BERT into Neural Machine Translation. ICLR 2020 - [c28]Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu:
On Layer Normalization in the Transformer Architecture. ICML 2020: 10524-10533 - [c27]Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang:
I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations. IJCAI 2020: 2669-2675 - [i32]Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang:
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. CoRR abs/2001.02378 (2020) - [i31]Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu:
On Layer Normalization in the Transformer Architecture. CoRR abs/2002.04745 (2020) - [i30]Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu:
Incorporating BERT into Neural Machine Translation. CoRR abs/2002.06823 (2020) - [i29]Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu:
Invertible Image Rescaling. CoRR abs/2005.05650 (2020) - [i28]Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu:
MC-BERT: Efficient Language Pre-Training via a Meta Controller. CoRR abs/2006.05744 (2020) - [i27]Guolin Ke, Di He, Tie-Yan Liu:
Rethinking Positional Encoding in Language Pre-training. CoRR abs/2006.15595 (2020) - [i26]Yunzhen Feng, Runtian Zhai, Di He, Liwei Wang, Bin Dong:
Transferred Discrepancy: Quantifying the Difference Between Representations. CoRR abs/2007.12446 (2020) - [i25]Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu:
Taking Notes on the Fly Helps BERT Pre-training. CoRR abs/2008.01466 (2020) - [i24]Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang:
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training. CoRR abs/2009.03294 (2020)
2010 – 2019
- 2019
- [c26]Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, Tie-Yan Liu:
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input. AAAI 2019: 3723-3730 - [c25]Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, Tie-Yan Liu:
Non-Autoregressive Machine Translation with Auxiliary Regularization. AAAI 2019: 5377-5384 - [c24]Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, Tao Qin:
Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder. AAAI 2019: 5466-5473 - [c23]ChengYue Gong, Xu Tan, Di He, Tao Qin:
Sentence-Wise Smooth Regularization for Sequence to Sequence Learning. AAAI 2019: 6449-6456 - [c22]Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu:
Multilingual Neural Machine Translation with Language Clustering. EMNLP/IJCNLP (1) 2019: 963-973 - [c21]Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Machine Translation With Weakly Paired Documents. EMNLP/IJCNLP (1) 2019: 4374-4383 - [c20]Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao Qin, Liwei Wang, Tie-Yan Liu:
Hint-Based Training for Non-Autoregressive Machine Translation. EMNLP/IJCNLP (1) 2019: 5707-5712 - [c19]Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu:
Representation Degeneration Problem in Training Natural Language Generation Models. ICLR (Poster) 2019 - [c18]Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, Tie-Yan Liu:
Multilingual Neural Machine Translation with Knowledge Distillation. ICLR (Poster) 2019 - [c17]Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu:
Efficient Training of BERT by Progressively Stacking. ICML 2019: 2337-2346 - [c16]Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie:
Towards a Deep and Unified Understanding of Deep Neural Models in NLP. ICML 2019: 2454-2463 - [c15]Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen:
Deliberation Learning for Image-to-Image Translation. IJCAI 2019: 2484-2490 - [c14]Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhi-Hong Deng:
Fast Structured Decoding for Sequence Models. NeurIPS 2019: 3011-3020 - [c13]Yingce Xia, Xu Tan, Fei Tian, Fei Gao, Di He, Weicong Chen, Yang Fan, Linyuan Gong, Yichong Leng, Renqian Luo, Yiren Wang, Lijun Wu, Jinhua Zhu, Tao Qin, Tie-Yan Liu:
Microsoft Research Asia's Systems for WMT19. WMT (2) 2019: 424-433 - [i23]Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, Tie-Yan Liu:
Non-Autoregressive Machine Translation with Auxiliary Regularization. CoRR abs/1902.10245 (2019) - [i22]Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, Tie-Yan Liu:
Multilingual Neural Machine Translation with Knowledge Distillation. CoRR abs/1902.10461 (2019) - [i21]Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Liwei Wang:
A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems. CoRR abs/1905.11675 (2019) - [i20]Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John E. Hopcroft, Liwei Wang:
Adversarially Robust Generalization Just Requires More Unlabeled Data. CoRR abs/1906.00555 (2019) - [i19]Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-Yan Liu:
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View. CoRR abs/1906.02762 (2019) - [i18]Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu:
Representation Degeneration Problem in Training Natural Language Generation Models. CoRR abs/1907.12009 (2019) - [i17]Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu:
Multilingual Neural Machine Translation with Language Clustering. CoRR abs/1908.09324 (2019) - [i16]Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao Qin, Liwei Wang, Tie-Yan Liu:
Hint-Based Training for Non-Autoregressive Machine Translation. CoRR abs/1909.06708 (2019) - [i15]Jinchen Xuan, Yunchang Yang, Ze Yang, Di He, Liwei Wang:
On the Anomalous Generalization of GANs. CoRR abs/1909.12638 (2019) - [i14]Zhiqing Sun, Zhuohan Li, Haoqing Wang, Zi Lin, Di He, Zhi-Hong Deng:
Fast Structured Decoding for Sequence Models. CoRR abs/1910.11555 (2019) - [i13]Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Liwei Wang:
Defective Convolutional Layers Learn Robust CNNs. CoRR abs/1911.08432 (2019) - 2018
- [c12]Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu:
Double Path Networks for Sequence to Sequence Learning. COLING 2018: 3064-3074 - [c11]Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter. EMNLP 2018: 3602-3611 - [c10]Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu:
Towards Binary-Valued Gates for Robust LSTM Training. ICML 2018: 3001-3010 - [c9]Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu:
Dense Information Flow for Neural Machine Translation. NAACL-HLT 2018: 1294-1303 - [c8]ChengYue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu:
FRAGE: Frequency-Agnostic Word Representation. NeurIPS 2018: 1341-1352 - [c7]Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu:
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation. NeurIPS 2018: 7955-7965 - [i12]Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu:
Dense Information Flow for Neural Machine Translation. CoRR abs/1806.00722 (2018) - [i11]Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu:
Towards Binary-Valued Gates for Robust LSTM Training. CoRR abs/1806.02988 (2018) - [i10]Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu:
Double Path Networks for Sequence to Sequence Learning. CoRR abs/1806.04856 (2018) - [i9]Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter. CoRR abs/1809.00120 (2018) - [i8]ChengYue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu:
FRAGE: Frequency-Agnostic Word Representation. CoRR abs/1809.06858 (2018) - [i7]ChengYue Gong, Xu Tan, Di He, Tao Qin:
Sentence-wise Smooth Regularization for Sequence to Sequence Learning. CoRR abs/1812.04784 (2018) - [i6]Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, Tie-Yan Liu:
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input. CoRR abs/1812.09664 (2018) - 2017
- [c6]Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu:
Decoding with Value Networks for Neural Machine Translation. NIPS 2017: 178-187 - [c5]Di He, Aadharsh Kannan, Tie-Yan Liu, R. Preston McAfee, Tao Qin, Justin M. Rao:
Scale Effects in Web Search. WINE 2017: 294-310 - 2016
- [c4]Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma:
Dual Learning for Machine Translation. NIPS 2016: 820-828 - [i5]Fei Tian, Bin Gao, Di He, Tie-Yan Liu:
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves. CoRR abs/1604.02038 (2016) - [i4]Yingce Xia, Di He, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma:
Dual Learning for Machine Translation. CoRR abs/1611.00179 (2016) - 2014
- [c3]Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang:
Generalized second price auction with probabilistic broad match. EC 2014: 39-56 - [i3]Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang:
Generalized Second Price Auction with Probabilistic Broad Match. CoRR abs/1404.3828 (2014) - [i2]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search. CoRR abs/1406.0728 (2014) - 2013
- [j1]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
Online learning for auction mechanism in bandit setting. Decis. Support Syst. 56: 379-386 (2013) - [c2]Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu:
A Theoretical Analysis of NDCG Type Ranking Measures. COLT 2013: 25-54 - [c1]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
A Game-Theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search. IJCAI 2013: 206-212 - [i1]Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, Wei Chen:
A Theoretical Analysis of NDCG Type Ranking Measures. CoRR abs/1304.6480 (2013)
Coauthor Index
[c62] [c59] [i70] [i66] [c55] [c53] [c48] [c47] [c44] [i54] [i53] [i52] [i51] [i48] [c40] [c39] [c38] [c37] [c36] [c34] [c33] [c32] [i43] [i42] [i41] [i39] [i38] [i37] [i35] [c31] [c29] [c28] [i31] [i30] [i29] [i28] [i27] [i25] [i24] [c26] [c25] [c22] [c21] [c20] [c19] [c18] [c17] [c13] [i23] [i22] [i19] [i18] [i17] [i16] [c12] [c11] [c10] [c9] [c8] [c7] [i12] [i11] [i10] [i9] [i8] [i6] [c6] [c5] [c4] [i5] [i4] [c3] [i3] [i2] [j1] [c2] [c1] [i1]
[i81] [i80] [c65] [c63] [c62] [c61] [c59] [c58] [i79] [i78] [i77] [i76] [i74] [i72] [i70] [i67] [i66] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [i65] [i64] [i61] [i59] [i58] [i57] [c46] [c44] [c43] [c41] [i54] [i51] [i50] [i48] [i47] [i45] [c37] [c35] [c33] [i44] [i43] [i35] [i34] [c30] [c28] [i32] [i31] [i28] [i26] [i24] [c20] [c19] [c17] [i21] [i20] [i19] [i18] [i16] [i15] [i13] [c10] [c8] [i11] [i8] [c6] [c4] [i4] [c3] [i3] [i2] [j1] [c2] [c1] [i1]
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