Machine Learning Landscapes

该项目将涉及神经网络的机器学习景观的特征。用于探索分子能量景观的理论和数值方法将应用于由人工神经网络损失函数定义的非凸景观。特别是，我们将研究状态密度的类似物来定义热力学量和速率常数。由此产生的见解将用于定义解决方案的组合，为从分子几何优化到临床医生诊断支持的应用提供卓越的预测能力。

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.