Publications

Preprints and Submitted

• J. Zhu, H. Huang, Z. Lin, J. Liang, Z. Tang, K. Almubarak, M. Alharthi, B. An, J. He, X. Wu, F. Yu, J. Chen, Z. Ma, Y. Du, Y. Hu, H. Zhang, E. Alghamdi, L. Zhang, R. Sun, H. Li, J. Xu, B. Wang. Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion. Submitted to ACL Rolling Review - August 2024.

• J. He, L. Liu and R. Tsai. Data-induced Multiscale Losses and Efficient Multirate Gradient Descent Schemes. ArXiv:2402.03021, 2024.

• J. He, T. Mao and J. Xu. Expressivity and Approximation Properties of Deep Neural Networks with Activation. ArXiv:2312.16483, 2023.

• J. He and J. Xu. Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions. ArXiv:2312.14276, 2023.

• J. He. On the Optimal Expressive Power of ReLU DNNs and Its Application in Approximation with Kolmogorov Superposition Theorem. ArXiv:2308.05509, 2023.

Published and Accepted

• J. Liang, Z. Cai, J. Zhu, H. Huang, K. Zong, B. An, M. Alharthi, J. He, L. Zhang, H. Li, B. Wang and J. Xu. Alignment at Pre-training! Towards Native Alignment for Arabic LLMs. Accepted for NeurIPS 2024.

• Y. Yang and J. He: Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss. Accepted for ICML 2024, ArXiv:2402.00152, 2024.

• H. Huang, F. Yu, J. Zhu, X. Sun, H. Cheng, D. Song, Z. Chen, M. Alharthi, B. An, J. He, Z. Liu, Z. Zhang, J. Chen, J. Li, B. Wang, L. Zhang, R. Sun, X. Wan, H. Li, J. Xu. AceGPT, Localizing Large Language Models in Arabic. Accepted for NAACL 2024, ArXiv:2309.12053, 2024.

• J. He, X. Liu and J. Xu. MgNO: Efficient Parameterization of Linear Operators via Multigrid. The Twelfth International Conference on Learning Representations (ICLR 2024). [Video Record], [ArXiv], [ResearchGate].

• L. Liu, J. He and R. Tsai. Linear Regression on Manifold Structured Data: The Impact of Extrinsic Geometry on Solutions. Topological, Algebraic and Geometric Learning Workshops at ICML2023. PMLR 221:557-576, Published PDF 2023. [ArXiv].

• J. Zhu*, J. He* and Q. Huang. An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations. Computational Geosciences 2023. DOI:10.1007/s10596-023-10211-8 [ArXiv].

• J. Zhu, J. He, L. Zhang and J. Xu. FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting. Journal of Computational Science 69: 102005, 2023. https:doi.org10.1016j.jocs.2023.102005 [ArXiv].

• J. He, J. Xu, L. Zhang and J. Zhu. An Interpretive Constrained Linear Model for ResNet and MgNet. Neural Networks. 162: 384-392, 2023. https:doi.org10.1016j.neunet.2023.03.011 [ArXiv].

• J. He, R. Tsai and R. Ward. Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Space. Research in the Mathematical Sciences. 10(13), 2023. https:doi.org10.1007s40687-023-00378-y [ArXi].

• J. He, L. Li and J. Xu. ReLU Deep Neural Networks from the Hierarchical Basis Perspective. Computers & Mathematics with Applications. 120: 105-114, 2022. https:doi.org10.1016j.camwa.2022.06.006. [ArXiv]

• J. He, L. Li and J. Xu. Approximation Properties of Deep ReLU CNNs. Research in the Mathematical Sciences. 9(38), 2022. https:doi.org10.1007s40687-022-00336-0. [Springer Nature SharedIt] [ArXiv]

• Q. Chen, W. Hao and J. He. Power Series Expansion Neural Network. Journal of Computational Science. 59, 2022. https:doi.org10.1016j.jocs.2021.101552. [ArXiv]

• Q. Chen, W. Hao and J. He. A Weight Initialization Based on the Linear Product Structure for Neural Networks. Applied Mathematics and Computation. 415, 2022. https:doi.org10.1016j.amc.2021.126722. [ArXiv]

• J. He, X. Jia, J. Xu, L. Zhang and L. Zhao. Make Regularization Effective in Training Sparse CNN. Computational Optimization and Applications. 77: 163–182, 2020. https:doi.org10.1007s10589-020-00202-1. [ArXiv]

• J. He, L. Li, J. Xu, and C. Zheng. ReLU Deep Neural Networks and Linear Finite Elements. Journal of Computational Mathematics. 38(3): 502-527, 2020. https:doi:10.4208/jcm.1810-m2018-0096. [ArXiv] [ESI Highly Cited Paper in Mathematics (November/December 2022)]

• J. He, K. Hu and J. Xu. Generalized Gaffney Inequality and Discrete Compactness for Discrete Differential Forms. Numerische Mathematik. 143: 781–795, 2019. https:doi.org10.1007s00211-019-01076-0. [ArXiv]

• J. He and J. Xu. MgNet: A Unified Framework of Multigrid and Convolutional Neural Network. Science China Mathematics. 62(7): 1331–1354, 2019. https:doi.org10.1007s11425-019-9547-2. [ArXiv]

*: equal contributions