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GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Hoki Kim, Jaewook Lee, Sungyoon Lee
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Advancing gradient based neural network training
Neural Parallel Sci. Comput., 1999Summary: Most of neural network applications rely on the fundamental approximation property of feedforward networks. Supervised learning is a means of implementing this approximate mapping. In a realistic problem setting, a mechanism is needed to devise this learning process based on available data, starting from choosing an appropriate set of ...
Jennie Si, Guian Zhou
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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2022Ye Tian, Xingyi Zhang, Kay Chen Tan
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Gradient-Enhanced Physics-Informed Neural Networks for Forward and Inverse PDE Problems
Computer Methods in Applied Mechanics and Engineering, 2022Lu Lu, George Karniadakis, Xuhui Meng
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Fully Decoupled Neural Network Learning Using Delayed Gradients
IEEE Transactions on Neural Networks and Learning Systems, 2022Yi Wang, Zhiping Lin, Huiping Zhuang
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A Self-Adaptive Gradient Descent Search Algorithm for Fully-Connected Neural Networks
Neurocomputing, 2022Yu Xue, Jiayu Liang
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GRADIENT ADVERSARIAL TRAINING OF NEURAL NETWORKS
2022SINHA AYAN TUHINENDU +3 more
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Gradient Amplification: An Efficient Way to Train Deep Neural Networks
Big Data Mining and Analytics, 2020Yi Pan, Chunyan Ji, Sunitha Basodi
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Deterministic Convergence of an Online Gradient Method for BP Neural Networks
IEEE Transactions on Neural Networks, 2005Zhengxue Li
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