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ORCIDZecheng Zhang
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2020 – today
- 2025
[j16]Zecheng Zhang, Wing Tat Leung
, Hayden Schaeffer:
A discretization-invariant extension and analysis of some deep operator networks. J. Comput. Appl. Math. 456: 116226 (2025)- 2024
- [j15]Shiwei Zhou, Yangzhong Wu, Chu Wang, Huayu Lu, Zecheng Zhang, Zijin Liu, Yongdeng Lei, Fu Chen:
Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach. Comput. Electron. Agric. 218: 108725 (2024) - [j14]Na Ou, Zecheng Zhang, Guang Lin:
A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems. J. Comput. Phys. 510: 113067 (2024) - [j13]Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers. Neural Networks 180: 106707 (2024) - [c5]Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen:
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. ICLR 2024 - [i40]Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin:
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. CoRR abs/2402.15406 (2024) - [i39]Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta, Kaidi Cao, Vid Kocijan, Jure Leskovec, Matthias Fey:
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning. CoRR abs/2404.00776 (2024) - [i38]Zecheng Zhang:
MODNO: Multi Operator Learning With Distributed Neural Operators. CoRR abs/2404.02892 (2024) - [i37]Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation. CoRR abs/2404.12355 (2024) - [i36]Xirui Peng, Qiming Xu, Zheng Feng, Haopeng Zhao, Lianghao Tan, Yan Zhou, Zecheng Zhang, Chenwei Gong, Yingqiao Zheng:
Automatic News Generation and Fact-Checking System Based on Language Processing. CoRR abs/2405.10492 (2024) - [i35]Cangqing Wang, Mingxiu Sui, Dan Sun, Zecheng Zhang, Yan Zhou:
Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees. CoRR abs/2405.13290 (2024) - [i34]Qianhui Wan, Zecheng Zhang, Liheng Jiang, Zhaoqi Wang, Yan Zhou:
Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model. CoRR abs/2406.13987 (2024) - [i33]Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Philip Torr, Bernard Ghanem, Guohao Li:
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. CoRR abs/2407.01511 (2024) - [i32]Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec:
RelBench: A Benchmark for Deep Learning on Relational Databases. CoRR abs/2407.20060 (2024) - [i31]Peiyuan Chen, Zecheng Zhang, Yiping Dong, Li Zhou, Han Wang:
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion. CoRR abs/2408.07303 (2024) - [i30]Zecheng Zhang:
Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach. CoRR abs/2408.12129 (2024) - [i29]Jingmin Sun, Zecheng Zhang, Hayden Schaeffer:
LeMON: Learning to Learn Multi-Operator Networks. CoRR abs/2408.16168 (2024) - [i28]Yuxuan Liu, Jingmin Sun, Xinjie He, Griffin Pinney, Zecheng Zhang, Hayden Schaeffer:
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics. CoRR abs/2409.09811 (2024) - [i27]Derek Jollie, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer:
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model. CoRR abs/2409.11609 (2024) - [i26]Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer:
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study. CoRR abs/2410.00357 (2024) - 2023
- [j12]Yalchin Efendiev, Wing Tat Leung, Wenyuan Li, Zecheng Zhang:
Hybrid explicit-implicit learning for multiscale problems with time dependent source. Commun. Nonlinear Sci. Numer. Simul. 120: 107081 (2023) - [j11]Guang Lin, Christian Moya, Zecheng Zhang:
Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks. Eng. Appl. Artif. Intell. 125: 106689 (2023) - [j10]Guang Lin, Christian Moya, Zecheng Zhang:
B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD. J. Comput. Phys. 473: 111713 (2023) - [j9]Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang:
Multi-agent Reinforcement Learning Aided Sampling Algorithms for a Class of Multiscale Inverse Problems. J. Sci. Comput. 96(2): 55 (2023) - [j8]Lu Peng, Zecheng Zhang, Xianyi Wang, Weiyi Qiu, Liqian Zhou, Hui Xiao, Chunxiuzi Liu, Shaohua Tang, Zhiwei Qin, Jiakun Jiang, Zengru Di, Yu Liu:
Theoretical perspective on synthetic man-made life: Learning from the origin of life. Quant. Biol. 11(4): 376-394 (2023) - [c4]Zecheng Zhang, Xianfeng Han, Guoqiang Xiao:
IFA-Net: Isomerous Feature-aware Network for Single-view 3D Reconstruction. IJCNN 2023: 1-8 - [i25]Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou:
Fast Replica Exchange Stochastic Gradient Langevin Dynamics. CoRR abs/2301.01898 (2023) - [i24]Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen:
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. CoRR abs/2307.08123 (2023) - [i23]Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer:
A discretization-invariant extension and analysis of some deep operator networks. CoRR abs/2307.09738 (2023) - [i22]Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer:
Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE. CoRR abs/2308.14188 (2023) - [i21]Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers. CoRR abs/2309.16816 (2023) - [i20]Guang Lin, Na Ou, Zecheng Zhang, Zhidong Zhang:
Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors. CoRR abs/2310.01541 (2023) - [i19]Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer:
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. CoRR abs/2310.18888 (2023) - 2022
- [j7]Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang:
Computational multiscale method for parabolic wave approximations in heterogeneous media. Appl. Math. Comput. 425: 127044 (2022) - [j6]Guang Lin, Yating Wang, Zecheng Zhang:
Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN. J. Comput. Phys. 460: 111173 (2022) - [j5]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
Efficient hybrid explicit-implicit learning for multiscale problems. J. Comput. Phys. 467: 111326 (2022) - [j4]Wing Tat Leung, Guang Lin, Zecheng Zhang:
NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems. J. Comput. Phys. 470: 111539 (2022) - [j3]Navjot Singh, Zecheng Zhang, Xiaoxiao Wu, Naijing Zhang, Siyuan Zhang, Edgar Solomonik:
Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels. J. Parallel Distributed Comput. 169: 269-285 (2022) - [c3]Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, Jiawei Han:
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction. NAACL-HLT 2022: 2395-2409 - [i18]Guang Li, Christian Moya, Zecheng Zhang:
On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. CoRR abs/2206.06536 (2022) - [i17]Yalchin Efendiev, Wing Tat Leung, Wenyuan Li, Zecheng Zhang:
Hybrid explicit-implicit learning for multiscale problems with time dependent source. CoRR abs/2208.06790 (2022) - [i16]Na Ou, Zecheng Zhang, Guang Lin:
A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems. CoRR abs/2210.17048 (2022) - [i15]Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer:
BelNet: Basis enhanced learning, a mesh-free neural operator. CoRR abs/2212.07336 (2022) - 2021
- [j2]Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang:
A multi-stage deep learning based algorithm for multiscale model reduction. J. Comput. Appl. Math. 394: 113506 (2021) - [j1]Boris Chetverushkin, Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang:
Computational multiscale methods for quasi-gas dynamic equations. J. Comput. Phys. 440: 110352 (2021) - [i14]Liu Liu, Tieyong Zeng, Zecheng Zhang:
A deep neural network approach on solving the linear transport model under diffusive scaling. CoRR abs/2102.12408 (2021) - [i13]Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang:
Computational multiscale methods for parabolic wave approximations in heterogeneous media. CoRR abs/2104.02283 (2021) - [i12]Guang Lin, Yating Wang, Zecheng Zhang:
Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. CoRR abs/2107.06330 (2021) - [i11]Wing Tat Leung, Guang Lin, Zecheng Zhang:
NH-PINN: Neural homogenization based physics-informed neural network for multiscale problems. CoRR abs/2108.12942 (2021) - [i10]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
HEI: hybrid explicit-implicit learning for multiscale problems. CoRR abs/2109.02147 (2021) - [i9]Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, Jiawei Han:
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction. CoRR abs/2109.12093 (2021) - [i8]Guang Lin, Zecheng Zhang, Zhidong Zhang:
Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. CoRR abs/2111.02285 (2021) - [i7]Guang Lin, Christian Moya, Zecheng Zhang:
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. CoRR abs/2111.02484 (2021) - 2020
- [c2]Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek F. Abdelzaher:
paper2repo: GitHub Repository Recommendation for Academic Papers. WWW 2020: 629-639 - [i6]Zecheng Zhang, Eric T. Chung, Yalchin Efendiev, Wing Tat Leung:
Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations. CoRR abs/2004.04308 (2020) - [i5]Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek F. Abdelzaher:
paper2repo: GitHub Repository Recommendation for Academic Papers. CoRR abs/2004.06059 (2020) - [i4]Boris Chetverushkin, Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang:
Computational multiscale methods for quasi-gas dynamic equations. CoRR abs/2009.00068 (2020) - [i3]Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang:
A multi-stage deep learning based algorithm for multiscale modelreduction. CoRR abs/2009.11341 (2020) - [i2]Eric T. Chung, Yalchin Efendiev, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang:
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem. CoRR abs/2011.08954 (2020)
2010 – 2019
- 2019
- [c1]Yuxin Xiao, Zecheng Zhang, Carl Yang, Chengxiang Zhai:
Non-local Attention Learning on Large Heterogeneous Information Networks. IEEE BigData 2019: 978-987 - [i1]Zecheng Zhang, Xiaoxiao Wu, Naijing Zhang, Siyuan Zhang, Edgar Solomonik:
Enabling Distributed-Memory Tensor Completion in Python using New Sparse Tensor Kernels. CoRR abs/1910.02371 (2019)
Coauthor Index
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last updated on 2024-11-06 20:26 CET by the dblp team
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