Matched interface and boundary method for elasticity interface problems. - Bao Wang 001; Kelin Xia; Guo-Wei Wei 001 (J. Comput. Appl. Math., 2015)

Second order method for solving 3D elasticity equations with complex interfaces. - Bao Wang 001; Kelin Xia; Guo-Wei Wei 001 (J. Comput. Phys., 2015)

Object-oriented persistent homology. - Bao Wang 001; Guo-Wei Wei 001 (J. Comput. Phys., 2016)

Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies. - Bao Wang 001; Zhixiong Zhao; Duc Duy Nguyen; Guo-Wei Wei 001 (CoRR, 2017)

Deep Learning for Real Time Crime Forecasting. - Bao Wang 001; Duo Zhang; Duanhao Zhang; P. Jeffrey Brantingham; Andrea L. Bertozzi (CoRR, 2017)

Deep Learning for Real-Time Crime Forecasting and its Ternarization. - Bao Wang 001; Penghang Yin; Andrea L. Bertozzi; P. Jeffrey Brantingham; Stanley J. Osher; Jack Xin (CoRR, 2017)

Finite volume formulation of the MIB method for elliptic interface problems. - Yin Cao; Bao Wang 001; Kelin Xia; Guo-Wei Wei 001 (J. Comput. Appl. Math., 2017)

ESES: Software for Eulerian solvent excluded surface. - Beibei Liu; Bao Wang 001; Rundong Zhao; Yiying Tong; Guo-Wei Wei 001 (J. Comput. Chem., 2017)

Accurate, robust, and reliable calculations of Poisson-Boltzmann binding energies. - Duc Duy Nguyen; Bao Wang 001; Guo-Wei Wei 001 (J. Comput. Chem., 2017)

Deep Neural Nets with Interpolating Function as Output Activation. - Bao Wang 001; Xiyang Luo; Zhen Li 029; Wei Zhu 007; Zuoqiang Shi; Stanley J. Osher (NeurIPS, 2018)

Deep Learning with Data Dependent Implicit Activation Function. - Bao Wang 001; Xiyang Luo; Zhen Li 029; Wei Zhu 007; Zuoqiang Shi; Stanley J. Osher (CoRR, 2018)

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data. - Bao Wang 001; Xiyang Luo; Fangbo Zhang; Baichuan Yuan; Andrea L. Bertozzi; P. Jeffrey Brantingham (CoRR, 2018)

Laplacian Smoothing Gradient Descent. - Stanley J. Osher; Bao Wang 001; Penghang Yin; Xiyang Luo; Minh Pham 003; Alex Tong Lin (CoRR, 2018)

Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization. - Bao Wang 001; Alex Tong Lin; Zuoqiang Shi; Wei Zhu 007; Penghang Yin; Andrea L. Bertozzi; Stanley J. Osher (CoRR, 2018)

Mathematical Analysis of Adversarial Attacks. - Zehao Dou; Stanley J. Osher; Bao Wang 001 (CoRR, 2018)

EnResNet: ResNet Ensemble via the Feynman-Kac Formalism. - Bao Wang 001; Binjie Yuan; Zuoqiang Shi; Stanley J. Osher (CoRR, 2018)

Breaking the polar-nonpolar division in solvation free energy prediction. - Bao Wang 001; Chengzhang Wang; Kedi Wu; Guo-Wei Wei 001 (J. Comput. Chem., 2018)

Scientific data interpolation with low dimensional manifold model. - Wei Zhu 007; Bao Wang 001; Richard C. Barnard; Cory D. Hauck; Frank Jenko; Stanley J. Osher (J. Comput. Phys., 2018)

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. - Bao Wang 001; Zuoqiang Shi; Stanley J. Osher (NeurIPS, 2019)

SOS-EW: System for Overdose Spike Early Warning Using Drug Mover's Distance-Based Hawkes Processes. - Wen-Hao Chiang; Baichuan Yuan; Hao Li; Bao Wang 001; Andrea L. Bertozzi; Jeremy G. Carter; Brad Ray; George O. Mohler (PKDD/ECML Workshops (1), 2019)

A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting. - Zhijian Li; Xiyang Luo; Bao Wang 001; Andrea L. Bertozzi; Jack Xin (WCGO, 2019)

A Deterministic Approach to Avoid Saddle Points. - Lisa Maria Kreusser; Stanley J. Osher; Bao Wang 001 (CoRR, 2019)

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. - Bao Wang 001; Quanquan Gu; March Boedihardjo; Farzin Barekat; Stanley J. Osher (CoRR, 2019)

Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning. - Bao Wang 001; Stanley J. Osher (CoRR, 2019)

Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning. - Wei Zhu 007; Qiang Qiu 001; Bao Wang 001; Jianfeng Lu 001; Guillermo Sapiro; Ingrid Daubechies (SIAM J. Math. Data Sci., 2019)

Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets. - Thu Dinh; Bao Wang 001; Andrea L. Bertozzi; Stanley J. Osher; Jack Xin (LOD (2), 2020)

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. - Bao Wang 001; Quanquan Gu; March Boedihardjo; Lingxiao Wang 001; Farzin Barekat; Stanley J. Osher (MSML, 2020)

MomentumRNN: Integrating Momentum into Recurrent Neural Networks. - Tan M. Nguyen; Richard G. Baraniuk; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (NeurIPS, 2020)

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. - Bao Wang 001; Tan M. Nguyen; Andrea L. Bertozzi; Richard G. Baraniuk; Stanley J. Osher (CoRR, 2020)

Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets. - Thu Dinh; Bao Wang 001; Andrea L. Bertozzi; Stanley J. Osher (CoRR, 2020)

Exploring Private Federated Learning with Laplacian Smoothing. - Zhicong Liang; Bao Wang 001; Quanquan Gu; Stanley J. Osher; Yuan Yao (CoRR, 2020)

An Integrated Approach to Produce Robust Models with High Efficiency. - Zhijian Li; Bao Wang 001; Jack Xin (CoRR, 2020)

EnResNet: ResNets Ensemble via the Feynman-Kac Formalism for Adversarial Defense and Beyond. - Bao Wang 001; Binjie Yuan; Zuoqiang Shi; Stanley J. Osher (SIAM J. Math. Data Sci., 2020)

Stability and Generalization of Decentralized Stochastic Gradient Descent. - Tao Sun 005; Dongsheng Li 001; Bao Wang 001 (AAAI, 2021)

Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning. - James N. Brundage; Vai Suliafu; Jake A. Bergquist; Brian Zenger; Lindsay C. Rupp; Bao Wang 001; Rob S. MacLeod (CinC, 2021)

An Integrated Approach to Produce Robust Deep Neural Network Models with High Efficiency. - Zhijian Li; Bao Wang 001; Jack Xin (LOD, 2021)

FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. - Tan M. Nguyen; Vai Suliafu; Stanley J. Osher; Long Chen; Bao Wang 001 (NeurIPS, 2021)

Heavy Ball Neural Ordinary Differential Equations. - Hedi Xia; Vai Suliafu; Hangjie Ji; Tan M. Nguyen; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (NeurIPS, 2021)

How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies. - Bao Wang 001; Hedi Xia; Tan M. Nguyen; Stanley J. Osher (CoRR, 2021)

Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization. - Tao Sun 005; Huaming Ling; Zuoqiang Shi; Dongsheng Li 001; Bao Wang 001 (CoRR, 2021)

Efficient and Reliable Overlay Networks for Decentralized Federated Learning. - Yifan Hua; Kevin Miller; Andrea L. Bertozzi; Chen Qian; Bao Wang 001 (CoRR, 2021)

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. - Bao Wang 001; Difan Zou; Quanquan Gu; Stanley J. Osher (SIAM J. Sci. Comput., 2021)

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography. - Ti Bai; Biling Wang; Dan Nguyen; Bao Wang 001; Bin Dong 001; Wenxiang Cong; Mannudeep K. Kalra; Steve B. Jiang (IEEE Trans. Medical Imaging, 2021)

Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling. - Carson Hu; Guang Lin 001; Bao Wang 001; Meng Yue; Jack Xin (AI4I, 2022)

Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction. - Yunling Zheng; Carson Hu; Guang Lin 001; Meng Yue; Bao Wang 001; Jack Xin (ICASSP, 2022)

GRAND++: Graph Neural Diffusion with A Source Term. - Matthew Thorpe; Tan Minh Nguyen; Hedi Xia; Thomas Strohmer; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (ICLR, 2022)

Adaptive Random Walk Gradient Descent for Decentralized Optimization. - Tao Sun 005; Dongsheng Li 001; Bao Wang 001 (ICML, 2022)

Adversarial Attacks on Deep Temporal Point Process. - Samira Khorshidi; Bao Wang 001; George O. Mohler (ICMLA, 2022)

Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. - Tan Minh Nguyen; Richard G. Baraniuk; Robert M. Kirby; Stanley J. Osher; Bao Wang 001 (MSML, 2022)

Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks. - Tao Sun 005; Dongsheng Li 001; Bao Wang 001 (NeurIPS, 2022)

Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs. - Justin M. Baker; Elena Cherkaev; Akil Narayan 001; Bao Wang 001 (CoRR, 2022)

Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. - Justin M. Baker; Hedi Xia; Yiwei Wang; Elena Cherkaev; Akil Narayan 001; Long Chen; Jack Xin; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (CoRR, 2022)

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. - Bao Wang 001; Tan M. Nguyen; Tao Sun 005; Andrea L. Bertozzi; Richard G. Baraniuk; Stanley J. Osher (SIAM J. Imaging Sci., 2022)

Efficient and Reliable Overlay Networks for Decentralized Federated Learning. - Yifan Hua; Kevin S. Miller; Andrea L. Bertozzi; Chen Qian; Bao Wang 001 (SIAM J. Appl. Math., 2022)

Adaptive and Implicit Regularization for Matrix Completion. - Zhemin Li; Tao Sun 005; Hongxia Wang; Bao Wang 001 (SIAM J. Imaging Sci., 2022)

Implicit Graph Neural Networks: A Monotone Operator Viewpoint. - Justin M. Baker; Qingsong Wang; Cory D. Hauck; Bao Wang 001 (ICML, 2023)

Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. - Tao Sun 005; Qingsong Wang; Dongsheng Li 001; Bao Wang 001 (ICML, 2023)

Pairwise Learning with Adaptive Online Gradient Descent. - Tao Sun 005; Qingsong Wang; Yunwen Lei; Dongsheng Li 001; Bao Wang 001 (Trans. Mach. Learn. Res., 2023)

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum. - Mengqi Hu; Yifei Lou; Bao Wang 001; Ming Yan 006; Xiu Yang; Qiang Ye 003 (J. Sci. Comput., 2023)

Decentralized Federated Averaging. - Tao Sun 005; Dongsheng Li 001; Bao Wang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs. - Justin M. Baker; Elena Cherkaev; Akil Narayan 001; Bao Wang 001 (J. Sci. Comput., 2023)

Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs. - Justin M. Baker; Qingsong Wang; Martin Berzins; Thomas Strohmer; Bao Wang 001 (AISTATS, 2024)

Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks. - Shih-Hsin Wang; Yung-Chang Hsu; Justin M. Baker; Andrea L. Bertozzi; Jack Xin; Bao Wang 001 (ICLR, 2024)

An Explicit Frame Construction for Normalizing 3D Point Clouds. - Justin M. Baker; Shih-Hsin Wang; Tommaso de Fernex; Bao Wang 001 (ICML, 2024)

Deep Learning with Data Privacy via Residual Perturbation. - Wenqi Tao; Huaming Ling; Zuoqiang Shi; Bao Wang 001 (CoRR, 2024)

Learning to Control the Smoothness of Graph Convolutional Network Features. - Shih-Hsin Wang; Justin M. Baker; Cory D. Hauck; Bao Wang 001 (CoRR, 2024)

Improving Deep Neural Networks' Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum. - Bao Wang 001; Qiang Ye 003 (IEEE Trans. Neural Networks Learn. Syst., 2024)

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules. - Shih-Hsin Wang; Yuhao Huang; Justin M. Baker; Yuan-En Sun; Qi Tang; Bao Wang 001 (ICLR, 2025)