mlsanityData to 7 Oct 2026

Survey on Memory-Augmented Neural Networks: Cognitive Insights to AI Applications

arXiv 2312.06141 ↗First posted 11 Dec 2023cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

18 of 35 references.

References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  8. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  10. 2014Neural Turing Machinescited by 630
  11. 2022Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learningcited by 553
  12. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
6 more
  1. 2022REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memorycited by 122
  2. 2022Task-aware Retrieval with Instructionscited by 107
  3. 2022Retrieval Augmented Classification for Long-Tail Visual Recognitioncited by 106
  4. 2022Retrieval Augmented Visual Question Answering with Outside Knowledgecited by 93
  5. 2021One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrievalcited by 46
  6. 2022Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactionscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2025Memento: Fine-tuning LLM Agents without Fine-tuning LLMscited by 119
  2. 2024Human-inspired Perspectives: A Survey on AI Long-term Memorycited by 12
  3. 2025Evaluating Long-Term Memory for Long-Context Question Answeringcited by 8
  4. 2026UniT: Unified Multimodal Chain-of-Thought Test-time Scalingcited by 8
  5. 2025Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI Systemcited by 6
  6. 2024Prospector Heads: Generalized Feature Attribution for Large Models & Datacited by 1
  7. 2025EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defensecited by 0