Bao Wang
Orcid: 0000-0002-4848-4791Affiliations:
- University of Utah, Department of Mathematics and Scientific Computing and Imaging Institute, Salt Lake City, UT, USA
- Michigan State University, East Lansing, MI, USA (PhD 2016)
According to our database1,
Bao Wang
authored at least 67 papers
between 2015 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Improving Deep Neural Networks' Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum.
IEEE Trans. Neural Networks Learn. Syst., September, 2024
CoRR, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs.
J. Sci. Comput., May, 2023
IEEE Trans. Pattern Anal. Mach. Intell., April, 2023
Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum.
J. Sci. Comput., April, 2023
Trans. Mach. Learn. Res., 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
2022
SIAM J. Imaging Sci., December, 2022
SIAM J. Appl. Math., August, 2022
SIAM J. Imaging Sci., 2022
Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization.
Proceedings of the Mathematical and Scientific Machine Learning, 2022
Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction.
Proceedings of the IEEE International Conference on Acoustics, 2022
Proceedings of the 5th International Conference on Artificial Intelligence for Industries, 2022
2021
IEEE Trans. Medical Imaging, 2021
SIAM J. Sci. Comput., 2021
CoRR, 2021
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization.
CoRR, 2021
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies.
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
An Integrated Approach to Produce Robust Deep Neural Network Models with High Efficiency.
Proceedings of the Machine Learning, Optimization, and Data Science, 2021
Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
EnResNet: ResNets Ensemble via the Feynman-Kac Formalism for Adversarial Defense and Beyond.
SIAM J. Math. Data Sci., 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM.
Proceedings of Mathematical and Scientific Machine Learning, 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets.
Proceedings of the Machine Learning, Optimization, and Data Science, 2020
2019
Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning.
SIAM J. Math. Data Sci., 2019
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning.
CoRR, 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM.
CoRR, 2019
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting.
Proceedings of the Optimization of Complex Systems: Theory, 2019
SOS-EW: System for Overdose Spike Early Warning Using Drug Mover's Distance-Based Hawkes Processes.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2018
J. Comput. Phys., 2018
J. Comput. Chem., 2018
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization.
CoRR, 2018
CoRR, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
J. Comput. Chem., 2017
J. Comput. Appl. Math., 2017
Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies.
CoRR, 2017
2016
2015
J. Comput. Phys., 2015
J. Comput. Appl. Math., 2015