Diffusion models learn to reverse the progressive noising of a data
dist...
Neural networks are powerful functions with widespread use, but the
theo...
DST methods achieve state-of-the-art results in sparse neural network
tr...
Stacking many layers to create truly deep neural networks is arguably wh...
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) ...
The logit outputs of a feedforward neural network at initialization are
...
An important propertyfor deep neural networks to possess is the ability ...
Scenario-based approaches have been receiving a huge amount of attention...
Theoretical results show that neural networks can be approximated by Gau...
The Feynman-Kac formula provides a way to understand solutions to ellipt...
We introduce a deep neural network based method for solving a class of
e...
We prove the precise scaling, at finite depth and width, for the mean an...
We study products of random matrices in the regime where the number of t...
We consider the problem of estimating a large rank-one tensor
u^⊗ k∈( R...