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Found 4 papers, 2 papers with code
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
We consider the optimisation of large and shallow neural networks via gradient flow, where the output of each hidden node is scaled by some positive parameter.
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Under this model, we show that each layer of the infinite-width neural network can be characterised by two simple quantities: a non-negative scalar parameter and a L\'evy measure on the positive reals.
$α$-Stable convergence of heavy-tailed infinitely-wide neural networks
We consider infinitely-wide multi-layer perceptrons (MLPs) which are limits of standard deep feed-forward neural networks.
At the edge of a one-dimensional jellium
We consider a one-dimensional classical Wigner jellium, not necessarily charge neutral, for which the electrons are allowed to exist beyond the support of the background charge.
Point Processes
Probability
Mathematical Physics
Mathematical Physics
Primary 82B05, 60K35, 60G55, Secondary 82D05, 62G30, 60G70
