Batch Normalized Recurrent Neural Networks (1510.01378v1)

Abstract: Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to train and difficult to parallelize. Recent work has shown that normalizing intermediate representations of neural networks can significantly improve convergence rates in feedforward neural networks . In particular, batch normalization, which uses mini-batch statistics to standardize features, was shown to significantly reduce training time. In this paper, we show that applying batch normalization to the hidden-to-hidden transitions of our RNNs doesn't help the training procedure. We also show that when applied to the input-to-hidden transitions, batch normalization can lead to a faster convergence of the training criterion but doesn't seem to improve the generalization performance on both our LLMling and speech recognition tasks. All in all, applying batch normalization to RNNs turns out to be more challenging than applying it to feedforward networks, but certain variants of it can still be beneficial.

• The paper demonstrates that applying batch normalization to input-to-hidden transitions accelerates RNN training convergence.
• Experimental evaluations on language modeling and speech recognition reveal faster training yet heightened overfitting.
• The study highlights the challenges of adapting normalization from feedforward networks to the unique dynamics of recurrent architectures.

An Analysis of Batch Normalized Recurrent Neural Networks

The exploration of recurrent neural networks (RNNs) through batch normalization provides intriguing insights into the intricacies of training these networks, particularly in the context of sequential data processing. The paper under review explores the application of batch normalization techniques to RNNs, contrasting this with the typical deployment in feedforward neural networks where it has been acknowledged to significantly enhance convergence rates.

Core Contributions

1. Batch Normalization in RNNs: This work primarily investigates the interaction of batch normalization within the architecture of RNNs, particularly how it can be leveraged (or not) to impact training efficiency. The authors analyze two key transitions—hidden-to-hidden and input-to-hidden transitions—and their susceptibility to batch normalization.
2. Experimental Evaluation: The paper provides empirical results across two domains—LLMing and speech recognition. It determines that batch normalization applied to input-to-hidden transitions can expedite the convergence of training criteria, although it does not enhance generalization performance across these tasks.
3. Challenges and Variants: A salient finding from this research is the nuanced challenge of applying batch normalization to RNNs compared to feedforward networks. Through experimentation, the authors identify that certain variants do exhibit beneficial properties, albeit in specific configurations and tasks.

Numerical Results and Claims

In the LLMing task utilizing the Penn Treebank dataset, batch normalization exhibited faster training convergence, yet it resulted in increased overfitting as evidenced by the training and validation perplexities. Similarly, in the Wall Street Journal speech corpus task, batch normalized RNNs showed promise in learning curves but demanded careful regularization due to overfitting tendencies.

Implications and Speculation on Future Work

The implications of this work are twofold; practically, there is potential for more efficient training of RNNs in sequential and temporal data tasks if batch normalization techniques are judiciously applied. From a theoretical standpoint, it underscores the complexity of stabilizing recurrent architectures through normalization, a topic ripe for further methodological inquiry.

Future directions may explore expanding batch normalization to more nuanced forms of whitening, or adapting it to other components of the RNN framework beyond input-to-hidden transitions. Additionally, research could investigate adaptive techniques that account for the overfitting observed in current experiments.

Conclusion

This paper presents a critical assessment of batch normalization within RNNs, revealing the contextual dependencies and challenges inherent in optimizing recurrent models. Although the application in RNNs is not straightforward and demands further exploration, the insights provided by this paper are a valuable contribution to the ongoing discourse around deep learning optimization methodologies. The understanding and manipulation of normalization in RNNs present a promising future research trajectory, with the potential to robustly enhance sequential data modeling.