Universal approximation theorems are the foundations of classical neural...
Reservoir computing approximation and generalization bounds are proved f...
In this article we study high-dimensional approximation capacities of sh...
A universal kernel is constructed whose sections approximate any causal ...
This article studies deep neural network expression rates for optimal
st...
This article investigates the use of random feature neural networks for
...
Deep neural networks (DNNs) with ReLU activation function are proved to ...
We study the expression rates of deep neural networks (DNNs for short) f...
Echo state networks (ESNs) have been recently proved to be universal
app...
A new explanation of geometric nature of the reservoir computing phenome...
Stochastic gradient descent (SGD) type optimization schemes are fundamen...
The notion of memory capacity, originally introduced for echo state and
...
Recently, so-called full-history recursive multilevel Picard (MLP)
appro...
This work studies approximation based on single-hidden-layer feedforward...
Recently, artificial neural networks (ANNs) in conjunction with stochast...
We analyze the practices of reservoir computing in the framework of
stat...
The universal approximation properties with respect to L ^p -type criter...