Di He

According to our database, Di He authored at least 93 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Do Efficient Transformers Really Save Computation?

Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang

CoRR, 2024

Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks.

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin

CoRR, 2024

DOF: Accelerating High-order Differential Operators with Forward Propagation.

Ruichen Li, Chuwei Wang, Haotian Ye, Di He, Liwei Wang

CoRR, 2024

Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation.

Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Di He, Jingjing Xu, Zhi Zhang, Hongxia Yang, Liwei Wang

CoRR, 2024

Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness.

Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang

CoRR, 2024

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.

Qingsi Lai, Lin Yao, Zhifeng Gao, Siyuan Liu, Hongshuai Wang, Shuqi Lu, Di He, Liwei Wang, Cheng Wang, Guolin Ke

CoRR, 2024

2023

REST: Retrieval-Based Speculative Decoding.

Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He

CoRR, 2023

CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity.

Pengyun Yue, Hanzhen Zhao, Cong Fang, Di He, Liwei Wang, Zhouchen Lin, Song-Chun Zhu

CoRR, 2023

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo.

Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang

CoRR, 2023

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+.

Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, Guolin Ke

CoRR, 2023

3D Molecular Generation via Virtual Dynamics.

Shuqi Lu, Lin Yao, Xi Chen, Hang Zheng, Di He, Guolin Ke

CoRR, 2023

Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers.

Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller

CoRR, 2023

Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective.

Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, Liwei Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests.

Bohang Zhang, Guhao Feng, Yiheng Du, Di He, Liwei Wang

Proceedings of the International Conference on Machine Learning, 2023

Rethinking the Expressive Power of GNNs via Graph Biconnectivity.

Bohang Zhang, Shengjie Luo, Liwei Wang, Di He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Denoising Masked Autoencoders Help Robust Classification.

Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

One Transformer Can Understand Both 2D & 3D Molecular Data.

Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets.

Haiyang Wang, Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, Liwei Wang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks.

Huishuai Zhang, Da Yu, Yiping Lu, Di He

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Learning Physics-Informed Neural Networks without Stacked Back-propagation.

Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Robustness-Aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions.

Yibin Wang, Yichen Yang, Di He, Kun He

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Denoising Masked AutoEncoders are Certifiable Robust Vision Learners.

Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He

CoRR, 2022

Is L² Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?

Chuwei Wang, Shanda Li, Di He, Liwei Wang

CoRR, 2022

METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals.

Payal Bajaj, Chenyan Xiong, Guolin Ke, Xiaodong Liu, Di He, Saurabh Tiwary, Tie-Yan Liu, Paul Bennett, Xia Song, Jianfeng Gao

CoRR, 2022

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

CoRR, 2022

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective.

Bohang Zhang, Du Jiang, Di He, Liwei Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Online Training Through Time for Spiking Neural Networks.

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?

Chuwei Wang, Shanda Li, Di He, Liwei Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Your Transformer May Not be as Powerful as You Expect.

Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

HousE: Knowledge Graph Embedding with Householder Parameterization.

Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

Proceedings of the International Conference on Machine Learning, 2022

Boosting the Certified Robustness of L-infinity Distance Nets.

Bohang Zhang, Du Jiang, Di He, Liwei Wang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart.

Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Finding the Dominant Winning Ticket in Pre-Trained Language Models.

Zhuocheng Gong, Di He, Yelong Shen, Tie-Yan Liu, Weizhu Chen, Dongyan Zhao, Ji-Rong Wen, Rui Yan

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

Can Vision Transformers Perform Convolution?

Shanda Li, Xiangning Chen, Di He, Cho-Jui Hsieh

CoRR, 2021

First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track.

Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, Di He

CoRR, 2021

Do Transformers Really Perform Bad for Graph Representation?

Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

CoRR, 2021

Adversarial Training with Rectified Rejection.

Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu

CoRR, 2021

Transformers with Competitive Ensembles of Independent Mechanisms.

Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio

CoRR, 2021

LazyFormer: Self Attention with Lazy Update.

Chengxuan Ying, Guolin Ke, Di He, Tie-Yan Liu

CoRR, 2021

Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder.

Shuqi Lu, Chenyan Xiong, Di He, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk

CoRR, 2021

Revisiting Language Encoding in Learning Multilingual Representations.

Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, Liwei Wang, Tie-Yan Liu

CoRR, 2021

Towards Certifying 𝓁_∞ Robustness using Neural Networks with 𝓁_∞-dist Neurons.

Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang

CoRR, 2021

Do Transformers Really Perform Badly for Graph Representation?

Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding.

Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Open Catalyst Challenge 2021: Competition Report.

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

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons.

Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang

Proceedings of the 38th International Conference on Machine Learning, 2021

How could Neural Networks understand Programs?

Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu

Proceedings of the 38th International Conference on Machine Learning, 2021

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training.

Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang

Proceedings of the 38th International Conference on Machine Learning, 2021

Rethinking Positional Encoding in Language Pre-training.

Guolin Ke, Di He, Tie-Yan Liu

Proceedings of the 9th International Conference on Learning Representations, 2021

Taking Notes on the Fly Helps Language Pre-Training.

Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu

Proceedings of the 9th International Conference on Learning Representations, 2021

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder.

Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

Taking Notes on the Fly Helps BERT Pre-training.

Qiyu Wu, Chen Xing, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu

CoRR, 2020

Transferred Discrepancy: Quantifying the Difference Between Representations.

Yunzhen Feng, Runtian Zhai, Di He, Liwei Wang, Bin Dong

CoRR, 2020

MC-BERT: Efficient Language Pre-Training via a Meta Controller.

Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu

CoRR, 2020

I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations.

Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

On Layer Normalization in the Transformer Architecture.

Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu

Proceedings of the 37th International Conference on Machine Learning, 2020

Incorporating BERT into Neural Machine Translation.

Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

Proceedings of the 8th International Conference on Learning Representations, 2020

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius.

Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang

Proceedings of the 8th International Conference on Learning Representations, 2020

Invertible Image Rescaling.

Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Defective Convolutional Layers Learn Robust CNNs.

Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Liwei Wang

CoRR, 2019

On the Anomalous Generalization of GANs.

Jinchen Xuan, Yunchang Yang, Ze Yang, Di He, Liwei Wang

CoRR, 2019

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View.

Yiping Lu, Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-Yan Liu

CoRR, 2019

Adversarially Robust Generalization Just Requires More Unlabeled Data.

Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John E. Hopcroft, Liwei Wang

CoRR, 2019

A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems.

Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Liwei Wang

CoRR, 2019

Microsoft Research Asia's Systems for WMT19.

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

Proceedings of the Fourth Conference on Machine Translation, 2019

Fast Structured Decoding for Sequence Models.

Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhi-Hong Deng

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deliberation Learning for Image-to-Image Translation.

Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Towards a Deep and Unified Understanding of Deep Neural Models in NLP.

Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Training of BERT by Progressively Stacking.

Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the 36th International Conference on Machine Learning, 2019

Multilingual Neural Machine Translation with Knowledge Distillation.

Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, Tie-Yan Liu

Proceedings of the 7th International Conference on Learning Representations, 2019

Representation Degeneration Problem in Training Natural Language Generation Models.

Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the 7th International Conference on Learning Representations, 2019

Machine Translation With Weakly Paired Documents.

Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Multilingual Neural Machine Translation with Language Clustering.

Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Hint-Based Training for Non-Autoregressive Machine Translation.

Zhuohan Li, Zi Lin, Di He, Fei Tian, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder.

Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, Tao Qin

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Non-Autoregressive Machine Translation with Auxiliary Regularization.

Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, Tie-Yan Liu

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input.

Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, Tie-Yan Liu

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Sentence-Wise Smooth Regularization for Sequence to Sequence Learning.

ChengYue Gong, Xu Tan, Di He, Tao Qin

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation.

Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

FRAGE: Frequency-Agnostic Word Representation.

ChengYue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Dense Information Flow for Neural Machine Translation.

Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Towards Binary-Valued Gates for Robust LSTM Training.

Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the 35th International Conference on Machine Learning, 2018

Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter.

Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Double Path Networks for Sequence to Sequence Learning.

Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu

Proceedings of the 27th International Conference on Computational Linguistics, 2018

2017

Scale Effects in Web Search.

Di He, Aadharsh Kannan, Tie-Yan Liu, R. Preston McAfee, Tao Qin, Justin M. Rao

Proceedings of the Web and Internet Economics - 13th International Conference, 2017

Decoding with Value Networks for Neural Machine Translation.

Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves.

Fei Tian, Bin Gao, Di He, Tie-Yan Liu

CoRR, 2016

Dual Learning for Machine Translation.

Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Generalized second price auction with probabilistic broad match.

Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang

Proceedings of the ACM Conference on Economics and Computation, 2014

2013

Online learning for auction mechanism in bandit setting.

Di He, Wei Chen, Liwei Wang, Tie-Yan Liu

Decis. Support Syst., 2013

A Theoretical Analysis of NDCG Type Ranking Measures

Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, Wei Chen

CoRR, 2013

A Game-Theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search.

Di He, Wei Chen, Liwei Wang, Tie-Yan Liu

Proceedings of the IJCAI 2013, 2013

A Theoretical Analysis of NDCG Type Ranking Measures.

Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu

Proceedings of the COLT 2013, 2013