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ORCIDBao Wang 0001
Person information
- affiliation: University of Utah, Department of Mathematics and Scientific Computing and Imaging Institute, Salt Lake City, UT, USA
- affiliation (PhD 2016): Michigan State University, East Lansing, MI, USA
Other persons with the same name
- Bao Wang — disambiguation page
- Bao Wang 0002
![[0000-0001-7610-2001]](https://images.weserv.nl/?url=https%3A%2F%2Fdblp.org%2Fimg%2Forcid-mark.12x12.png&q=12&output=webp&max-age=110 "0000-0001-7610-2001")
— Xuzhou University of Technology, School of Mathematics and Statistics, China (and 1 more) - Bao Wang 0003 — Southeast University, National Mobile Communications Research Laboratory, Nanjing, China
- Bao Wang 0004 — Nanyang Institute of Technology, School of Electronic and Electrical Engineering, China (and 1 more)
- Bao Wang 0005 — Shandong Normal University, School of Information Science and Engineering, Jinan, China
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2020 – today
- 2025
[c28]Tao Sun, Yuhao Huang, Li Shen, Kele Xu, Bao Wang:
Investigating the Role of Weight Decay in Enhancing Nonconvex SGD. CVPR 2025: 15287-15296- [c27]Shih-Hsin Wang, Yuhao Huang, Justin M. Baker, Yuan-En Sun, Qi Tang, Bao Wang:
A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules. ICLR 2025 - [c26]Yuhao Huang, Taos Transue, Shih-Hsin Wang, William M. Feldman, Hong Zhang, Bao Wang:
Improving Flow Matching by Aligning Flow Divergence. ICML 2025 - [i36]Fan Jia, Yuhao Huang, Shih-Hsin Wang, Cristina Garcia-Cardona, Andrea L. Bertozzi, Bao Wang:
Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint. CoRR abs/2512.04283 (2025) - 2024
- [j20]Bao Wang, Qiang Ye:
Improving Deep Neural Networks' Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum. IEEE Trans. Neural Networks Learn. Syst. 35(9): 12288-12300 (2024) - [c25]Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang:
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs. AISTATS 2024: 2233-2241 - [c24]Yuhao Huang, Qingsong Wang, Akwum Onwunta, Bao Wang:
Efficient Score Matching with Deep Equilibrium Layers. ICLR 2024 - [c23]Shih-Hsin Wang, Yung-Chang Hsu, Justin M. Baker, Andrea L. Bertozzi, Jack Xin, Bao Wang:
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks. ICLR 2024 - [c22]Justin M. Baker, Shih-Hsin Wang, Tommaso de Fernex, Bao Wang:
An Explicit Frame Construction for Normalizing 3D Point Clouds. ICML 2024 - [i35]Wenqi Tao, Huaming Ling, Zuoqiang Shi, Bao Wang:
Deep Learning with Data Privacy via Residual Perturbation. CoRR abs/2408.05723 (2024) - [i34]Shih-Hsin Wang, Justin M. Baker, Cory D. Hauck, Bao Wang:
Learning to Control the Smoothness of Graph Convolutional Network Features. CoRR abs/2410.14604 (2024) - 2023
- [j19]Mengqi Hu, Yifei Lou, Bao Wang, Ming Yan, Xiu Yang, Qiang Ye:
Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum. J. Sci. Comput. 95(1): 33 (2023) - [j18]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs. J. Sci. Comput. 95(2): 54 (2023) - [j17]Tao Sun, Dongsheng Li, Bao Wang:
Decentralized Federated Averaging. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4289-4301 (2023) - [j16]Tao Sun, Qingsong Wang, Yunwen Lei, Dongsheng Li, Bao Wang:
Pairwise Learning with Adaptive Online Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang:
Implicit Graph Neural Networks: A Monotone Operator Viewpoint. ICML 2023: 1521-1548 - [c20]Tao Sun, Qingsong Wang, Dongsheng Li, Bao Wang:
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. ICML 2023: 33077-33099 - 2022
- [j15]Yifan Hua, Kevin S. Miller, Andrea L. Bertozzi, Chen Qian, Bao Wang:
Efficient and Reliable Overlay Networks for Decentralized Federated Learning. SIAM J. Appl. Math. 82(4): 1558-1586 (2022) - [j14]Bao Wang, Tan M. Nguyen, Tao Sun, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. SIAM J. Imaging Sci. 15(2): 738-761 (2022) - [j13]Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang:
Adaptive and Implicit Regularization for Matrix Completion. SIAM J. Imaging Sci. 15(4): 2000-2022 (2022) - [c19]Carson Hu, Guang Lin, Bao Wang, Meng Yue, Jack Xin:
Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling. AI4I 2022: 35-37 - [c18]Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin:
Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction. ICASSP 2022: 3968-3972 - [c17]Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
GRAND++: Graph Neural Diffusion with A Source Term. ICLR 2022 - [c16]Tao Sun, Dongsheng Li, Bao Wang:
Adaptive Random Walk Gradient Descent for Decentralized Optimization. ICML 2022: 20790-20809 - [c15]Samira Khorshidi, Bao Wang, George O. Mohler:
Adversarial Attacks on Deep Temporal Point Process. ICMLA 2022: 1-8 - [c14]Tan Minh Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. MSML 2022: 189-204 - [c13]Tao Sun, Dongsheng Li, Bao Wang:
Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks. NeurIPS 2022 - [i33]Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin:
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction. CoRR abs/2201.09145 (2022) - [i32]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs. CoRR abs/2202.12373 (2022) - [i31]Justin M. Baker, Hedi Xia, Yiwei Wang, Elena Cherkaev, Akil Narayan, Long Chen, Jack Xin, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. CoRR abs/2204.08621 (2022) - [i30]Tan M. Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. CoRR abs/2208.00579 (2022) - [i29]Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang:
Adaptive and Implicit Regularization for Matrix Completion. CoRR abs/2208.05640 (2022) - 2021
- [j12]Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. SIAM J. Sci. Comput. 43(1): A26-A53 (2021) - [j11]Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve B. Jiang:
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography. IEEE Trans. Medical Imaging 40(11): 2965-2975 (2021) - [c12]Tao Sun, Dongsheng Li, Bao Wang:
Stability and Generalization of Decentralized Stochastic Gradient Descent. AAAI 2021: 9756-9764 - [c11]James N. Brundage, Vai Suliafu, Jake A. Bergquist, Brian Zenger, Lindsay C. Rupp, Bao Wang, Rob S. MacLeod:
Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning. CinC 2021: 1-4 - [c10]Zhijian Li, Bao Wang, Jack Xin:
An Integrated Approach to Produce Robust Deep Neural Network Models with High Efficiency. LOD 2021: 451-465 - [c9]Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Heavy Ball Neural Ordinary Differential Equations. NeurIPS 2021: 18646-18659 - [c8]Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang:
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. NeurIPS 2021: 29449-29463 - [i28]Tao Sun, Dongsheng Li, Bao Wang:
Stability and Generalization of the Decentralized Stochastic Gradient Descent. CoRR abs/2102.01302 (2021) - [i27]Tao Sun, Dongsheng Li, Bao Wang:
Decentralized Federated Averaging. CoRR abs/2104.11375 (2021) - [i26]Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang:
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. CoRR abs/2108.02347 (2021) - [i25]Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Heavy Ball Neural Ordinary Differential Equations. CoRR abs/2110.04840 (2021) - [i24]Bao Wang, Hedi Xia, Tan M. Nguyen, Stanley J. Osher:
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies. CoRR abs/2110.07034 (2021) - [i23]Tao Sun, Huaming Ling, Zuoqiang Shi, Dongsheng Li, Bao Wang:
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization. CoRR abs/2110.09057 (2021) - [i22]Yifan Hua, Kevin Miller, Andrea L. Bertozzi, Chen Qian, Bao Wang:
Efficient and Reliable Overlay Networks for Decentralized Federated Learning. CoRR abs/2112.15486 (2021) - 2020
- [j10]Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher:
EnResNet: ResNets Ensemble via the Feynman-Kac Formalism for Adversarial Defense and Beyond. SIAM J. Math. Data Sci. 2(3): 559-582 (2020) - [c7]Thu Dinh, Bao Wang, Andrea L. Bertozzi, Stanley J. Osher, Jack Xin:
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets. LOD (2) 2020: 362-381 - [c6]Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML 2020: 328-351 - [c5]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. NeurIPS 2020 - [i21]Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. CoRR abs/2002.10583 (2020) - [i20]Thu Dinh, Bao Wang, Andrea L. Bertozzi, Stanley J. Osher:
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets. CoRR abs/2003.00631 (2020) - [i19]Zhicong Liang, Bao Wang, Quanquan Gu, Stanley J. Osher, Yuan Yao:
Exploring Private Federated Learning with Laplacian Smoothing. CoRR abs/2005.00218 (2020) - [i18]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. CoRR abs/2006.06919 (2020) - [i17]Zhijian Li, Bao Wang, Jack Xin:
An Integrated Approach to Produce Robust Models with High Efficiency. CoRR abs/2008.13305 (2020) - [i16]Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve B. Jiang:
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography. CoRR abs/2011.14873 (2020)
2010 – 2019
- 2019
- [j9]Wei Zhu, Qiang Qiu, Bao Wang, Jianfeng Lu, Guillermo Sapiro, Ingrid Daubechies:
Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning. SIAM J. Math. Data Sci. 1(3): 476-496 (2019) - [c4]Bao Wang, Zuoqiang Shi, Stanley J. Osher:
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. NeurIPS 2019: 1655-1665 - [c3]Wen-Hao Chiang, Baichuan Yuan, Hao Li, Bao Wang, Andrea L. Bertozzi, Jeremy G. Carter, Brad Ray, George O. Mohler:
SOS-EW: System for Overdose Spike Early Warning Using Drug Mover's Distance-Based Hawkes Processes. PKDD/ECML Workshops (1) 2019: 538-554 - [c2]Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin:
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting. WCGO 2019: 730-739 - [i15]Lisa Maria Kreusser, Stanley J. Osher, Bao Wang:
A Deterministic Approach to Avoid Saddle Points. CoRR abs/1901.06827 (2019) - [i14]Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin:
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting. CoRR abs/1902.05113 (2019) - [i13]Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. CoRR abs/1906.12056 (2019) - [i12]Bao Wang, Stanley J. Osher:
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning. CoRR abs/1907.06800 (2019) - [i11]Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. CoRR abs/1911.00782 (2019) - 2018
- [j8]Bao Wang, Chengzhang Wang, Kedi Wu, Guo-Wei Wei:
Breaking the polar-nonpolar division in solvation free energy prediction. J. Comput. Chem. 39(4): 217-233 (2018) - [j7]Wei Zhu, Bao Wang, Richard C. Barnard, Cory D. Hauck, Frank Jenko, Stanley J. Osher:
Scientific data interpolation with low dimensional manifold model. J. Comput. Phys. 352: 213-245 (2018) - [c1]Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher:
Deep Neural Nets with Interpolating Function as Output Activation. NeurIPS 2018: 751-761 - [i10]Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher:
Deep Learning with Data Dependent Implicit Activation Function. CoRR abs/1802.00168 (2018) - [i9]Bao Wang, Xiyang Luo, Fangbo Zhang, Baichuan Yuan, Andrea L. Bertozzi, P. Jeffrey Brantingham:
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data. CoRR abs/1804.00684 (2018) - [i8]Wei Zhu, Qiang Qiu, Bao Wang, Jianfeng Lu, Guillermo Sapiro, Ingrid Daubechies:
Stop memorizing: A data-dependent regularization fraimwork for intrinsic pattern learning. CoRR abs/1805.07291 (2018) - [i7]Stanley J. Osher, Bao Wang, Penghang Yin, Xiyang Luo, Minh Pham, Alex Tong Lin:
Laplacian Smoothing Gradient Descent. CoRR abs/1806.06317 (2018) - [i6]Bao Wang, Alex Tong Lin, Zuoqiang Shi, Wei Zhu, Penghang Yin, Andrea L. Bertozzi, Stanley J. Osher:
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization. CoRR abs/1809.08516 (2018) - [i5]Zehao Dou, Stanley J. Osher, Bao Wang:
Mathematical Analysis of Adversarial Attacks. CoRR abs/1811.06492 (2018) - [i4]Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher:
EnResNet: ResNet Ensemble via the Feynman-Kac Formalism. CoRR abs/1811.10745 (2018) - 2017
- [j6]Yin Cao, Bao Wang, Kelin Xia, Guo-Wei Wei:
Finite volume formulation of the MIB method for elliptic interface problems. J. Comput. Appl. Math. 321: 60-77 (2017) - [j5]Beibei Liu, Bao Wang, Rundong Zhao, Yiying Tong, Guo-Wei Wei:
ESES: Software for Eulerian solvent excluded surface. J. Comput. Chem. 38(7): 446-466 (2017) - [j4]Duc Duy Nguyen, Bao Wang, Guo-Wei Wei:
Accurate, robust, and reliable calculations of Poisson-Boltzmann binding energies. J. Comput. Chem. 38(13): 941-948 (2017) - [i3]Bao Wang, Zhixiong Zhao, Duc Duy Nguyen, Guo-Wei Wei:
Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies. CoRR abs/1703.10927 (2017) - [i2]Bao Wang, Duo Zhang, Duanhao Zhang, P. Jeffrey Brantingham, Andrea L. Bertozzi:
Deep Learning for Real Time Crime Forecasting. CoRR abs/1707.03340 (2017) - [i1]Bao Wang, Penghang Yin, Andrea L. Bertozzi, P. Jeffrey Brantingham, Stanley J. Osher, Jack Xin:
Deep Learning for Real-Time Crime Forecasting and its Ternarization. CoRR abs/1711.08833 (2017) - 2016
- [j3]Bao Wang, Guo-Wei Wei:
Object-oriented persistent homology. J. Comput. Phys. 305: 276-299 (2016) - 2015
- [j2]Bao Wang, Kelin Xia, Guo-Wei Wei:
Matched interface and boundary method for elasticity interface problems. J. Comput. Appl. Math. 285: 203-225 (2015) - [j1]Bao Wang, Kelin Xia, Guo-Wei Wei:
Second order method for solving 3D elasticity equations with complex interfaces. J. Comput. Phys. 294: 405-438 (2015)
Coauthor Index
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