Dmytro Perekrestenko | DeepAI

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Léandre Eberhard
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∙ 07/26/2021

High-Dimensional Distribution Generation Through Deep Neural Networks

We show that every d-dimensional probability distribution of bounded sup...

249 Dmytro Perekrestenko, et al. ∙

∙ 06/30/2020

Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks

We present an explicit deep neural network construction that transforms ...

1 Dmytro Perekrestenko, et al. ∙

∙ 01/08/2019

Deep Neural Network Approximation Theory

Deep neural networks have become state-of-the-art technology for a wide ...

1 Philipp Grohs, et al. ∙

∙ 06/05/2018

The universal approximation power of finite-width deep ReLU networks

We show that finite-width deep ReLU neural networks yield rate-distortio...

1 Dmytro Perekrestenko, et al. ∙

∙ 03/07/2017

Faster Coordinate Descent via Adaptive Importance Sampling

Coordinate descent methods employ random partial updates of decision var...

0 Dmytro Perekrestenko, et al. ∙

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