Recent Advances in Recurrent Neural Networks

TL;DR

This paper surveys recent advances in recurrent neural networks, highlighting their ability to model sequential data and discussing challenges in learning long-term dependencies.

Contribution

It provides a comprehensive overview of RNN fundamentals, recent developments, and research challenges for both newcomers and professionals.

Findings

Summarizes key advances in RNN architectures and training methods.

Identifies ongoing challenges in learning long-term dependencies.

Provides insights into future research directions.

Abstract

Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.