Recurrent Batch NormalizationRecurrent Batch NormalizationCooijmans, Tim and Ballas, Nicolas and Laurent, César and Courville, Aaron2016

Paper summaryabhshkdzThis paper presents a re-parameterization of the LSTM to successfully apply batch normalization, which results in faster convergence and improved generalization on a several sequential tasks. Main contributions:
- Batch normalization is applied to the input to hidden and hidden to hidden projections.
- Separate statistics are maintained for each timestep, estimated over each minibatch during training and over the whole dataset during test.
- For generalization to longer sequences during test time, population statistics of time T\_max are used for all time steps beyond it.
- The cell state is left untouched so as not to hinder the gradient flow.
- Proper initialization of batch normalization parameters to avoid vanishing gradients.
- They plot norm of gradient of loss wrt hidden state at different time steps for different BN variance initializations. High variance ($\gamma = 1$) causes gradients to die quickly by driving activations to the saturation region.
- Initializing BN variance to 0.1 works well.
## Strengths
- Simple idea, the authors finally got it to work. Proper initialization of BN parameters and maintaining separate estimates for each time step play a key role.
## Weaknesses / Notes
- It would be useful in practice to put down a proper formulation for using batch normalization with variable-length training sequences.

This paper presents a re-parameterization of the LSTM to successfully apply batch normalization, which results in faster convergence and improved generalization on a several sequential tasks. Main contributions:
- Batch normalization is applied to the input to hidden and hidden to hidden projections.
- Separate statistics are maintained for each timestep, estimated over each minibatch during training and over the whole dataset during test.
- For generalization to longer sequences during test time, population statistics of time T\_max are used for all time steps beyond it.
- The cell state is left untouched so as not to hinder the gradient flow.
- Proper initialization of batch normalization parameters to avoid vanishing gradients.
- They plot norm of gradient of loss wrt hidden state at different time steps for different BN variance initializations. High variance ($\gamma = 1$) causes gradients to die quickly by driving activations to the saturation region.
- Initializing BN variance to 0.1 works well.
## Strengths
- Simple idea, the authors finally got it to work. Proper initialization of BN parameters and maintaining separate estimates for each time step play a key role.
## Weaknesses / Notes
- It would be useful in practice to put down a proper formulation for using batch normalization with variable-length training sequences.