Memory augmented Neural Networks: Analyzing memory augmented neural network architectures for incorporating external memory to enhance learning and reasoning

Abstract

Memory-augmented neural networks (MANNs) have emerged as a promising approach to enhance the learning and reasoning capabilities of neural networks by incorporating external memory. This paper provides a comprehensive review and analysis of various MANN architectures, focusing on their design principles, memory structures, and applications. We discuss key concepts such as memory addressing mechanisms, read and write operations, and training strategies. Furthermore, we examine the strengths and limitations of MANNs compared to traditional neural networks, highlighting their potential for addressing complex tasks that require memory retention and retrieval. Through a series of experiments and case studies, we demonstrate the effectiveness of MANNs in tasks such as language modeling, algorithm learning, and reasoning, showcasing their versatility and potential for future research directions.