Zobrazeno 1 - 10 of 1 372 pro vyhledávání: '"Krzyzak, A."'
1
Report
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Autor: Kohler, Michael, Krzyzak, Adam, Walter, Benjamin
Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between
Externí odkaz: http://arxiv.org/abs/2405.07619
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2
Report
Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
Autor: Kohler, Michael, Krzyzak, Adam, Sänger, Alisha
Image classification from independent and identically distributed random variables is considered. Image classifiers are defined which are based on a linear combination of deep convolutional networks with max-pooling layer. Here all the weights are le
Externí odkaz: http://arxiv.org/abs/2404.07128
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3
Report
On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent
Autor: Kohler, Michael, Krzyzak, Adam
One of the most recent and fascinating breakthroughs in artificial intelligence is ChatGPT, a chatbot which can simulate human conversation. ChatGPT is an instance of GPT4, which is a language model based on generative gredictive gransformers. So if
Externí odkaz: http://arxiv.org/abs/2312.17007
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4
Report
Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent
Autor: Kohler, Michael, Krzyzak, Adam
Estimation of a regression function from independent and identically distributed random variables is considered. The $L_2$ error with integration with respect to the design measure is used as an error criterion. Over-parametrized deep neural network
Externí odkaz: http://arxiv.org/abs/2210.01443
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9
Akademický článek
An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification
Autor: Guangyi Chen, Adam Krzyzak, Shen-en Qian
Publikováno v: Image Analysis and Stereology, Vol 43, Iss 2 (2024)
Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes.
Externí odkaz: https://doaj.org/article/f83187dd1ea6461dba1365e7293d438a
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