使用具有动态外部内存的神经网络的混合计算
Alex Graves1, Greg Wayne1, Malcolm Reynolds1
1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|October 13, 2016
概括
一个新的可差分神经计算机 (DNC) 模型将神经网络与外部内存集成,使复杂的数据操纵和学习成为可能. 这种人工智能的进步克服了传统神经网络在结构化推理和长期数据存储方面的局限性.
科学领域:
- 人工智能
- 机器学习
- 计算机科学
背景情况:
- 人工神经网络在传感和序列处理方面表现出色,但由于缺乏外部记忆,难以处理复杂的数据结构和长期记忆.
- 现有的神经网络模型在需要长时间变量表示和数据操纵的任务上是有限的.
研究的目的:
- 引入一种新的机器学习模型,即可区分的神经计算机 (DNC),能够与外部内存进行交互.
- 通过利用其外部内存能力来展示DNC学习和执行复杂的推理和结构化任务的能力.
主要方法:
- 开发了一个可区分的神经计算机 (DNC) 模型,将神经网络与读写外部内存矩阵结合起来.
- 通过监督学习来训练DNC进行推理和推断任务,并通过强化学习来执行目标导向的序列任务.
- 在基于合成和现实世界的图表问题和符号序列驱动的拼图上评估了DNC.
主要成果:
- 通过模仿自然语言推理和推理,
- 证明了最短路径的学习和图形链接推断,将其推广到运输网络和家族树.
- 通过符号序列指定改变目标的移动块拼图成功完成.
结论:
- 可区分神经计算机 (DNC) 通过结合外部内存来弥合神经网络和传统计算机之间的差距.
- DNC表现出以前无法通过标准神经网络解决的复杂,结构化的任务的能力.
- 这种进步为人工智能在需要复杂推理,数据操纵和长期记忆的领域开辟了新的可能性.
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