Memoria：通过人类启发的记忆架构解决宿命遗忘问题
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture
AI总结：
针对神经网络长期记忆中的宿命遗忘问题，受人类记忆机制启发提出Memoria记忆系统，在排序、语言建模和分类任务中超越传统方法，并展现出人类记忆特征。
摘要：
让神经网络进行长期记忆一直是一个长期存在的问题。尽管已经引入了多种外部记忆技术，但大多数都侧重于短期保留近期信息。无论信息多么重要，随着时间的推移，信息往往会被宿命地遗忘。我们提出了Memoria，一个受人类启发并应用多种神经科学和心理学理论的人工神经网络记忆系统。实验结果表明，Memoria在排序、语言建模和分类等多种任务中均有效，超越了传统技术。印记分析显示，Memoria表现出首因效应、近因效应和时间邻近效应，这些正是人类记忆的特征。
英文摘要：
Making neural networks remember over the long term has been a longstanding issue. Although several external memory techniques have been introduced, most focus on retaining recent information in the short term. Regardless of its importance, information tends to be fatefully forgotten over time. We present Memoria, a memory system for artificial neural networks, drawing inspiration from humans and applying various neuroscientific and psychological theories. The experimental results prove the effectiveness of Memoria in the diverse tasks of sorting, language modeling, and classification, surpassing conventional techniques. Engram analysis reveals that Memoria exhibits the primacy, recency, and temporal contiguity effects which are characteristics of human memory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
