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Found 10 papers, 1 papers with code
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.
Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
Recent results show that estimates defined by over-parametrized deep neural networks learned by applying gradient descent to a regularized empirical $L_2$ risk are universally consistent and achieve good rates of convergence.
Analysis of convolutional neural network image classifiers in a rotationally symmetric model
Convolutional neural network image classifiers are defined and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed.
On the rate of convergence of a classifier based on a Transformer encoder
Pattern recognition based on a high-dimensional predictor is considered.
Estimation of a regression function on a manifold by fully connected deep neural networks
Estimation of a regression function from independent and identically distributed data is considered.
Uncertainty Quantification in Case of Imperfect Models: A Review
Uncertainty quantification of complex technical systems is often based on a computer model of the system.
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.
Common Voice: A Massively-Multilingual Speech Corpus
To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On the rate of convergence of fully connected very deep neural network regression estimates
Recent results in nonparametric regression show that deep learning, i. e., neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure of the regression function hold.
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 $d$.
