Machine Learning Landscapes

机器学习景观

• 批准号: 2275899
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2275899
• 关键词: Machine; Learning; Landscapes

This project will involve the characterisation of machine learning landscapes for neural networks. The theory and numerical methods developed for exploring molecular energy landscapes will be applied to the non-convex landscapes defined by the loss function of an artificial neural network. In particular, we will investigate analogues of the density of states to define thermodynamic quantities and rate constants. The resulting insight will be used to define combinations of solutions that provide superior predictive power for applications ranging from molecular geometry optimisation to clinician diagnostic support.

该项目将涉及神经网络机器学习景观的特征。为探索分子能量景观而开发的理论和数值方法将应用于由人工神经网络的损失函数定义的非凸景观。特别是，我们将研究类似物的状态密度来定义热力学量和速率常数。由此产生的洞察力将用于定义解决方案的组合，这些解决方案为从分子几何优化到临床医生诊断支持的应用提供上级预测能力。