Understanding Neural Networks through Dynamics

通过动力学了解神经网络

• 批准号: EP/V046829/1
• 负责人: Jonathan Dawes
• 金额: $ 25.41万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV046829%2F1
• 关键词: Understanding; Neural; Networks; through; Dynamics

Artificial Neural Networks (ANNs) have recently moved back into the spotlight as tools to simulate, predict, and classify complex inputs. Our ability to use these new computational methods with confidence in applications demands that we develop a better fundamental understanding of why, how, and in what situations ANNs can be relied on. Reliance on such tools for decision support is increasing rapidly in fields from autonomous vehicles to policing, as is the list of notable examples of biases, failures, and injustices that can all be traced back to fundamental mathematical issues.There has been particular focus recently on reservoir computing, related to recurrent neural networks, in which most of the network connections form a single-layer recurrent 'dynamical reservoir' with fixed (usually randomly chosen) connections and training carried out by adjusting a small number of output weights. In this project the work will focus on a class of reservoir systems known as Echo State Networks (ESNs).This proposal will develop new fundamental understanding of structure in ESNs, and will develop previously-unexploited properties of ESNs as dynamical systems; in turn this will enable new applications of ESNs to physical systems, including climate dynamics.

人工神经网络（ANN）最近重新成为人们关注的焦点，作为模拟，预测和分类复杂输入的工具。我们有能力在应用中自信地使用这些新的计算方法，这要求我们对为什么、如何以及在什么情况下可以依赖人工神经网络有更好的基本理解。从自动驾驶汽车到警务等领域，对此类工具的决策支持的依赖正在迅速增加，和不公正，都可以追溯到基本的数学问题。最近特别关注水库计算，与递归神经网络，其中大多数网络连接形成具有固定（通常随机选择的）连接的单层循环“动态库”，并且通过调整少量输出权重来执行训练。在这个项目中，工作将集中在一类被称为回声状态网络（ESNs）的水库系统上。这个建议将发展对ESNs结构的新的基本理解，并将发展ESNs作为动力系统的以前未开发的特性;反过来，这将使ESNs在物理系统中的新应用成为可能，包括气候动力学。