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Batch-normalized recurrent highway networks
2017
2017 IEEE International Conference on Image Processing (ICIP)
Gradient control plays an important role in feed-forward networks applied to various computer vision tasks. Previous work has shown that Recurrent Highway Networks minimize the problem of vanishing or exploding gradients. They achieve this by setting the eigenvalues of the temporal Jacobian to 1 across the time steps. In this work, batch normalized recurrent highway networks are proposed to control the gradient flow in an improved way for network convergence. Specifically, the introduced model
doi:10.1109/icip.2017.8296359
dblp:conf/icip/ZhangNSPLS17
fatcat:dgrdgqdqqzajrfod7heefr45dq