Non-local Attention Learning on Large Heterogeneous Information Networks. - Yuxin Xiao; Zecheng Zhang; Carl Yang 001; Chengxiang Zhai (IEEE BigData, 2019)

Enabling Distributed-Memory Tensor Completion in Python using New Sparse Tensor Kernels. - Zecheng Zhang; Xiaoxiao Wu; Naijing Zhang; Siyuan Zhang; Edgar Solomonik (CoRR, 2019)

paper2repo: GitHub Repository Recommendation for Academic Papers. - Huajie Shao; Dachun Sun; Jiahao Wu; Zecheng Zhang; Aston Zhang; Shuochao Yao; Shengzhong Liu; Tianshi Wang; Chao Zhang 014; Tarek F. Abdelzaher (WWW, 2020)

Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations. - Zecheng Zhang; Eric T. Chung 001; Yalchin Efendiev; Wing Tat Leung (CoRR, 2020)

A multi-stage deep learning based algorithm for multiscale modelreduction. - Eric T. Chung 001; Wing Tat Leung; Sai-Mang Pun; Zecheng Zhang (CoRR, 2020)

Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem. - Eric T. Chung 001; Yalchin Efendiev; Wing Tat Leung; Sai-Mang Pun; Zecheng Zhang (CoRR, 2020)

A deep neural network approach on solving the linear transport model under diffusive scaling. - Liu Liu; Tieyong Zeng; Zecheng Zhang (CoRR, 2021)

Computational multiscale methods for parabolic wave approximations in heterogeneous media. - Eric T. Chung 001; Yalchin Efendiev; Sai-Mang Pun; Zecheng Zhang (CoRR, 2021)

Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. - Guang Lin 001; Yating Wang; Zecheng Zhang (CoRR, 2021)

HEI: hybrid explicit-implicit learning for multiscale problems. - Yalchin Efendiev; Wing Tat Leung; Guang Lin 001; Zecheng Zhang (CoRR, 2021)

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. - Guang Lin 001; Zecheng Zhang; Zhidong Zhang (CoRR, 2021)

Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2021)

A multi-stage deep learning based algorithm for multiscale model reduction. - Eric T. Chung 001; Wing Tat Leung; Sai-Mang Pun; Zecheng Zhang (J. Comput. Appl. Math., 2021)

Computational multiscale methods for quasi-gas dynamic equations. - Boris Chetverushkin; Eric T. Chung 001; Yalchin Efendiev; Sai-Mang Pun; Zecheng Zhang (J. Comput. Phys., 2021)

SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction. - Yuxin Xiao; Zecheng Zhang; Yuning Mao; Carl Yang 001; Jiawei Han 001 (NAACL-HLT, 2022)

Computational multiscale method for parabolic wave approximations in heterogeneous media. - Eric T. Chung 001; Yalchin Efendiev; Sai-Mang Pun; Zecheng Zhang (Appl. Math. Comput., 2022)

On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2022)

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems. - Na Ou; Zecheng Zhang; Guang Lin 001 (CoRR, 2022)

BelNet: Basis enhanced learning, a mesh-free neural operator. - Zecheng Zhang; Wing Tat Leung; Hayden Schaeffer (CoRR, 2022)

Efficient hybrid explicit-implicit learning for multiscale problems. - Yalchin Efendiev; Wing Tat Leung; Guang Lin 001; Zecheng Zhang (J. Comput. Phys., 2022)

NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems. - Wing Tat Leung; Guang Lin 001; Zecheng Zhang (J. Comput. Phys., 2022)

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN. - Guang Lin 001; Yating Wang; Zecheng Zhang (J. Comput. Phys., 2022)

Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels. - Navjot Singh; Zecheng Zhang; Xiaoxiao Wu; Naijing Zhang; Siyuan Zhang; Edgar Solomonik (J. Parallel Distributed Comput., 2022)

IFA-Net: Isomerous Feature-aware Network for Single-view 3D Reconstruction. - Zecheng Zhang; Xianfeng Han; Guoqiang Xiao 001 (IJCNN, 2023)

Fast Replica Exchange Stochastic Gradient Langevin Dynamics. - Guanxun Li; Guang Lin 001; Zecheng Zhang; Quan Zhou (CoRR, 2023)

PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers. - Yuxuan Liu; Zecheng Zhang; Hayden Schaeffer (CoRR, 2023)

Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors. - Guang Lin 001; Na Ou; Zecheng Zhang; Zhidong Zhang (CoRR, 2023)

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2023)

Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (Eng. Appl. Artif. Intell., 2023)

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD. - Guang Lin 001; Christian Moya; Zecheng Zhang (J. Comput. Phys., 2023)

Hybrid explicit-implicit learning for multiscale problems with time dependent source. - Yalchin Efendiev; Wing Tat Leung; Wenyuan Li 004; Zecheng Zhang (Commun. Nonlinear Sci. Numer. Simul., 2023)

Multi-agent Reinforcement Learning Aided Sampling Algorithms for a Class of Multiscale Inverse Problems. - Eric T. Chung 001; Wing Tat Leung; Sai-Mang Pun; Zecheng Zhang (J. Sci. Comput., 2023)

Theoretical perspective on synthetic man-made life: Learning from the origin of life. - Lu Peng; Zecheng Zhang; Xianyi Wang; Weiyi Qiu; Liqian Zhou; Hui Xiao; Chunxiuzi Liu; Shaohua Tang; Zhiwei Qin; Jiakun Jiang; Zengru Di; Yu Liu 064 (Quant. Biol., 2023)

Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. - Bowen Song; Soo Min Kwon; Zecheng Zhang; Xinyu Hu; Qing Qu 001; Liyue Shen (ICLR, 2024)

RelBench: A Benchmark for Deep Learning on Relational Databases. - Joshua Robinson 001; Rishabh Ranjan; Weihua Hu; Kexin Huang; Jiaqi Han; Alejandro Dobles; Matthias Fey; Jan Eric Lenssen; Yiwen Yuan; Zecheng Zhang; Xinwei He; Jure Leskovec (NeurIPS, 2024)

Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach. - Shiwei Zhou; Yangzhong Wu; Chu Wang; Huayu Lu; Zecheng Zhang; Zijin Liu; Yongdeng Lei; Fu Chen (Comput. Electron. Agric., 2024)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. - Christian Moya; Amirhossein Mollaali; Zecheng Zhang; Lu Lu; Guang Lin 001 (CoRR, 2024)

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning. - Weihua Hu; Yiwen Yuan; Zecheng Zhang; Akihiro Nitta; Kaidi Cao; Vid Kocijan; Jure Leskovec; Matthias Fey (CoRR, 2024)

MODNO: Multi Operator Learning With Distributed Neural Operators. - Zecheng Zhang (CoRR, 2024)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation. - Jingmin Sun; Yuxuan Liu; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

Automatic News Generation and Fact-Checking System Based on Language Processing. - Xirui Peng; Qiming Xu; Zheng Feng; Haopeng Zhao; Lianghao Tan; Yan Zhou; Zecheng Zhang; Chenwei Gong; Yingqiao Zheng (CoRR, 2024)

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees. - Cangqing Wang; Mingxiu Sui; Dan Sun; Zecheng Zhang; Yan Zhou (CoRR, 2024)

Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model. - Qianhui Wan; Zecheng Zhang; Liheng Jiang; Zhaoqi Wang; Yan Zhou (CoRR, 2024)

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. - Tianqi Xu; Linyao Chen; Dai-Jie Wu; Yanjun Chen; Zecheng Zhang; Xiang Yao; Zhiqiang Xie; Yongchao Chen; Shilong Liu; Bochen Qian; Philip Torr 001; Bernard Ghanem; et al. (CoRR, 2024)

Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion. - Peiyuan Chen; Zecheng Zhang; Yiping Dong; Li Zhou; Han Wang (CoRR, 2024)

Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach. - Zecheng Zhang (CoRR, 2024)

LeMON: Learning to Learn Multi-Operator Networks. - Jingmin Sun; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics. - Yuxuan Liu; Jingmin Sun; Xinjie He; Griffin Pinney; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model. - Derek Jollie; Jingmin Sun; Zecheng Zhang; Hayden Schaeffer (CoRR, 2024)

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study. - Hao Liu; Zecheng Zhang; Wenjing Liao; Hayden Schaeffer (CoRR, 2024)

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2024)

ContextGNN: Beyond Two-Tower Recommendation Systems. - Yiwen Yuan; Zecheng Zhang; Xinwei He; Akihiro Nitta; Weihua Hu; Dong Wang; Manan Shah; Shenyang Huang; Blaz Stojanovic; Alan Krumholz; Jan Eric Lenssen; Jure Leskovec; et al. (CoRR, 2024)

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems. - Na Ou; Zecheng Zhang; Guang Lin 001 (J. Comput. Phys., 2024)

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE. - Zecheng Zhang; Christian Moya; Wing Tat Leung; Guang Lin 001; Hayden Schaeffer (Multiscale Model. Simul., 2024)

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers. - Yuxuan Liu; Zecheng Zhang; Hayden Schaeffer (Neural Networks, 2024)

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. - Tianqi Xu; Linyao Chen; Dai-Jie Wu; Yanjun Chen; Zecheng Zhang; Xiang Yao; Zhiqiang Xie; Yongchao Chen; Shilong Liu; Bochen Qian; Anjie Yang; Zhaoxuan Jin; et al. (ACL (Findings), 2025)

ContextGNN: Beyond Two-Tower Recommendation Systems. - Yiwen Yuan; Zecheng Zhang; Xinwei He; Akihiro Nitta; Weihua Hu; Manan Shah; Blaz Stojanovic; Shenyang Huang; Jan Eric Lenssen; Jure Leskovec; Matthias Fey (ICLR, 2025)

A discretization-invariant extension and analysis of some deep operator networks. - Zecheng Zhang; Wing Tat Leung; Hayden Schaeffer (J. Comput. Appl. Math., 2025)

CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation. - Bowen Song; Zecheng Zhang; Zhaoxu Luo; Jason Hu; Wei Yuan; Jing Jia; Zhengxu Tang; Guanyang Wang; Liyue Shen (CoRR, 2025)

AVA: Attentive VLM Agent for Mastering StarCraft II. - Weiyu Ma; Yuqian Fu; Zecheng Zhang; Bernard Ghanem; Guohao Li 001 (CoRR, 2025)

Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees. - Zecheng Zhang; Hao Liu; Wenjing Liao; Guang Lin 001 (CoRR, 2025)

PyG 2.0: Scalable Learning on Real World Graphs. - Matthias Fey; Jinu Sunil; Akihiro Nitta; Rishi Puri; Manan Shah; Blaz Stojanovic; Ramona Bendias; Alexandria Barghi; Vid Kocijan; Zecheng Zhang; Xinwei He; Jan Eric Lenssen; et al. (CoRR, 2025)