Obtaining accurate solutions to the Schrödinger equation is the key
chal...
Particle localization and -classification constitute two of the most
fun...
Deep neural networks have become a highly accurate and powerful wavefunc...
Statistical learning theory provides bounds on the necessary number of
t...
Finding accurate solutions to the Schrödinger equation is the key unsolv...
In this effort, we derive a formula for the integral representation of a...
We consider neural network approximation spaces that classify functions
...
We study the phase reconstruction of signals f belonging to complex
Gaus...
Accurate numerical solutions for the Schrödinger equation are of utmost
...
We describe the new field of mathematical analysis of deep learning. Thi...
We study the computational complexity of (deterministic or randomized)
a...
The approximation of solutions to second order Hamilton–Jacobi–Bellman
(...
Artificial neural networks (ANNs) have become a very powerful tool in th...
We present a deep learning algorithm for the numerical solution of param...
Rate distortion theory is concerned with optimally encoding a given sign...
In recent work it has been established that deep neural networks are cap...
Recently, artificial neural networks (ANNs) in conjunction with stochast...
Recently, it has been proposed in the literature to employ deep neural
n...
Over the last few years deep artificial neural networks (DNNs) have very...
Neural network training is usually accomplished by solving a non-convex
...
Although for neural networks with locally Lipschitz continuous activatio...
We present a novel technique based on deep learning and set theory which...
Deep neural networks have become state-of-the-art technology for a wide ...
The development of new classification and regression algorithms based on...
Artificial neural networks (ANNs) have very successfully been used in
nu...
We show that finite-width deep ReLU neural networks yield rate-distortio...
Stochastic differential equations (SDEs) and the Kolmogorov partial
diff...
Deep convolutional neural networks (CNNs) used in practice employ potent...
Many practical machine learning tasks employ very deep convolutional neu...
First steps towards a mathematical theory of deep convolutional neural
n...
Wiatowski and Bölcskei, 2015, proved that deformation stability and
vert...