research
∙
06/14/2021
On the Sample Complexity of Learning with Geometric Stability
Many supervised learning problems involve high-dimensional data such as ...
share
research
∙
04/05/2021
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
This work presents a multilevel variant of Stein variational gradient de...
research
∙
02/02/2021
Depth separation beyond radial functions
High-dimensional depth separation results for neural networks show that ...
research
∙
07/28/2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Classical reduced models are low-rank approximations using a fixed basis...
research
∙
03/23/2020
Diffusion-based Deep Active Learning
The remarkable performance of deep neural networks depends on the availa...
research
∙
02/18/2018
Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys
Neural networks provide a rich class of high-dimensional, non-convex opt...
research
∙
12/22/2017