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Found 2 papers, 1 papers with code
Riemannian approach to batch normalization
Batch Normalization (BN) has proven to be an effective algorithm for deep neural network training by normalizing the input to each neuron and reducing the internal covariate shift.
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
An MDRNN up to a depth of 15 layers is successfully trained using HF, resulting in an improved performance for sequence labeling.
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