10 citations · 15 across the 6 of their papers we have counts for
7 papers
Random matrix theory and the loss surfaces of neural networks
Nicholas P Baskerville
Neural network models are one of the most successful approaches to machine learning, enjoying an enormous amount of development and research over recent years and finding concrete…
Universal characteristics of deep neural network loss surfaces from random matrix theory
Nicholas P Baskerville, Jonathan P Keating, Francesco Mezzadri +2
This paper considers several aspects of random matrix universality in deep neural networks. Motivated by recent experimental work, we use universal properties of random matrices re…
A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration
Nicholas P Baskerville
Determinantal points processes are a promising but relatively under-developed tool in machine learning and statistical modelling, being the canonical statistical example of distrib…
Appearance of Random Matrix Theory in Deep Learning
Nicholas P Baskerville, Diego Granziol, Jonathan P Keating
We investigate the local spectral statistics of the loss surface Hessians of artificial neural networks, where we discover excellent agreement with Gaussian Orthogonal Ensemble sta…
A spin-glass model for the loss surfaces of generative adversarial networks
Nicholas P Baskerville, Jonathan P Keating, Francesco Mezzadri +1
We present a novel mathematical model that seeks to capture the key design feature of generative adversarial networks (GANs). Our model consists of two interacting spin glasses, an…
A Random Matrix Theory Approach to Damping in Deep Learning
Diego Granziol, Nicholas Baskerville
We conjecture that the inherent difference in generalisation between adaptive and non-adaptive gradient methods in deep learning stems from the increased estimation noise in the fl…