Chris Mingard

According to our database, Chris Mingard authored at least 8 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Visualising Feature Learning in Deep Neural Networks by Diagonalizing the Forward Feature Map.

Yoonsoo Nam, Chris Mingard, Seok Hyeong Lee, Soufiane Hayou, Ard A. Louis

CoRR, 2024

Exploiting the equivalence between quantum neural networks and perceptrons.

Chris Mingard, Jessica Pointing, Charles London, Yoonsoo Nam, Ard A. Louis

CoRR, 2024

2023

Do deep neural networks have an inbuilt Occam's razor?

Chris Mingard, Henry Rees, Guillermo Valle Pérez, Ard A. Louis

CoRR, 2023

Automatic Gradient Descent: Deep Learning without Hyperparameters.

Jeremy Bernstein, Chris Mingard, Kevin Huang, Navid Azizan, Yisong Yue

CoRR, 2023

2022

Feature Learning and Signal Propagation in Deep Neural Networks.

Yizhang Lou, Chris E. Mingard, Soufiane Hayou

Proceedings of the International Conference on Machine Learning, 2022

2021

Is SGD a Bayesian sampler? Well, almost.

Chris Mingard, Guillermo Valle Pérez, Joar Skalse, Ard A. Louis

J. Mach. Learn. Res., 2021

The Equilibrium Hypothesis: Rethinking implicit regularization in Deep Neural Networks.

Yizhang Lou, Chris Mingard, Soufiane Hayou

CoRR, 2021

2019

Neural networks are a priori biased towards Boolean functions with low entropy.

Chris Mingard, Joar Skalse, Guillermo Valle Pérez, David Martínez-Rubio, Vladimir Mikulik, Ard A. Louis

CoRR, 2019