Survey on Memory-Augmented Neural Networks: Cognitive Insights to AI Applications
It provides a comprehensive overview for researchers interested in memory-based AI systems, but it is incremental as it synthesizes existing knowledge without introducing new methods or data.
This survey paper explores Memory-Augmented Neural Networks (MANNs) by examining how they integrate human-like memory processes into AI, covering various architectures and their applications in fields like Natural Language Processing and Computer Vision to enhance accuracy, efficiency, and reliability.
This paper explores Memory-Augmented Neural Networks (MANNs), delving into how they blend human-like memory processes into AI. It covers different memory types, like sensory, short-term, and long-term memory, linking psychological theories with AI applications. The study investigates advanced architectures such as Hopfield Networks, Neural Turing Machines, Correlation Matrix Memories, Memformer, and Neural Attention Memory, explaining how they work and where they excel. It dives into real-world uses of MANNs across Natural Language Processing, Computer Vision, Multimodal Learning, and Retrieval Models, showing how memory boosters enhance accuracy, efficiency, and reliability in AI tasks. Overall, this survey provides a comprehensive view of MANNs, offering insights for future research in memory-based AI systems.