Breaking Time Invariance: Assorted-Time Normalization for RNNs

TL;DR

This paper introduces Assorted-Time Normalization (ATN), a novel RNN normalization technique that incorporates multiple time steps to better capture temporal dependencies without adding trainable parameters.

Contribution

The paper proposes ATN, a normalization method that accounts for multiple time steps in RNNs, enhancing temporal modeling without increasing model complexity.

Findings

ATN improves performance on synthetic tasks like Adding, Copying, and Denoising.

ATN enhances language modeling results across various benchmarks.

Theoretical analysis confirms gradient stability and weight invariance.

Abstract

Methods such as Layer Normalization (LN) and Batch Normalization (BN) have proven to be effective in improving the training of Recurrent Neural Networks (RNNs). However, existing methods normalize using only the instantaneous information at one particular time step, and the result of the normalization is a preactivation state with a time-independent distribution. This implementation fails to account for certain temporal differences inherent in the inputs and the architecture of RNNs. Since these networks share weights across time steps, it may also be desirable to account for the connections between time steps in the normalization scheme. In this paper, we propose a normalization method called Assorted-Time Normalization (ATN), which preserves information from multiple consecutive time steps and normalizes using them. This setup allows us to introduce longer time dependencies into the traditional normalization methods without introducing any new trainable parameters. We present theoretical derivations for the gradient propagation and prove the weight scaling invariance property. Our experiments applying ATN to LN demonstrate consistent improvement on various tasks, such as Adding, Copying, and Denoise Problems and Language Modeling Problems.