Wei Tao
Orcid: 0000-0002-8273-6649Affiliations:
- Army Engineering University of PLA, Nanjing, China
According to our database1,
Wei Tao
authored at least 15 papers
between 2019 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Location and time embedded feature representation for spatiotemporal traffic prediction.
Expert Syst. Appl., 2024
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Neurocomputing, December, 2023
Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attacks.
CoRR, 2023
2022
Momentum Acceleration in the Individual Convergence of Nonsmooth Convex Optimization With Constraints.
IEEE Trans. Neural Networks Learn. Syst., 2022
Provable convergence of Nesterov's accelerated gradient method for over-parameterized neural networks.
Knowl. Based Syst., 2022
A convergence analysis of Nesterov's accelerated gradient method in training deep linear neural networks.
Inf. Sci., 2022
A high-resolution dynamical view on momentum methods for over-parameterized neural networks.
CoRR, 2022
2021
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods.
Proceedings of the 9th International Conference on Learning Representations, 2021
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
The Strength of Nesterov's Extrapolation in the Individual Convergence of Nonsmooth Optimization.
IEEE Trans. Neural Networks Learn. Syst., 2020
Primal Averaging: A New Gradient Evaluation Step to Attain the Optimal Individual Convergence.
IEEE Trans. Cybern., 2020
Comput. Networks, 2020
2019
Densely Connected Convolutional Networks With Attention LSTM for Crowd Flows Prediction.
IEEE Access, 2019