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On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent
One of the most recent and fascinating breakthroughs in artificial intelligence is ChatGPT, a chatbot which can simulate human conversation.
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data
A regression problem with dependent data is considered.
Estimation of a function of low local dimensionality by deep neural networks
Consequently, the rate of convergence of the estimate does not depend on its input dimension $d$, but on its local dimension $d^*$ and the DNNs are able to circumvent the curse of dimensionality in case that $d^*$ is much smaller than $d$.
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