Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
A neural network learns to classify different types of spacetime in general relativity according to their algebraic Petrov classification.
The ability of deep neural networks to generalize can be unraveled using path integral methods to compute their typical Boolean functions.
We generalise neural networks into a quantum framework, demonstrating the possibility of quantum auto-encoders and teleportation.
Percolation theory shows that the formation of giant clusters of neurons relies on a few parameters that could be measured experimentally.
Associative networks with different loads model the ability of the immune system to respond simultaneously to multiple distinct antigen invasions.