@inproceedings{
ayed2023overparameterised,
title={Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: {\textbackslash}{\textbackslash} 	Global Convergence Guarantees and Feature Learning},
author={Fadhel Ayed and Francois Caron and Paul Jung and Juho Lee and Hoil Lee and Hongseok Yang},
booktitle={NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning},
year={2023},
url={https://openreview.net/forum?id=QBOV4DqFh6}
}
We consider gradient-based optimisation of wide, shallow neural networks with hidden-node ouputs scaled by positive scale parameters. The scale parameters are non-identical, differing from classical Neural Tangent Kernel (NTK) parameterisation. We prove that, for large networks, with high probability, gradient flow converges to a global minimum AND can learn features, unlike in the NTK regime.
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: 
 	Global Convergence Guarantees and Feature Learning
Fadhel Ayed Francois Caron Paul Jung Juho Lee Hoil Lee Hongseok Yang