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Akademický článek

Telemedicine in Germany During the COVID-19 Pandemic: Multi-Professional National Survey

Autor: Peine, Arne, Paffenholz, Pia, Martin, Lukas, Dohmen, Sandra, Marx, Gernot, Loosen, Sven H

Publikováno v: Journal of Medical Internet Research, Vol 22, Iss 8, p e19745 (2020)

BackgroundIn an effort to contain the effects of the coronavirus disease (COVID-19) pandemic, health care systems worldwide implemented telemedical solutions to overcome staffing, technical, and infrastructural limitations. In Germany, a multitude of

Externí odkaz: https://doaj.org/article/dd354b83644149be8089eeee9b0b9320

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LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data

Autor: Laux, Hendrik, Mededovic, Emil, Hallawa, Ahmed, Martin, Lukas, Peine, Arne, Schmeink, Anke

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent i

Externí odkaz: http://arxiv.org/abs/2312.09727

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EVO-RL: Evolutionary-Driven Reinforcement Learning

Autor: Hallawa, Ahmed, Born, Thorsten, Schmeink, Anke, Dartmann, Guido, Peine, Arne, Martin, Lukas, Iacca, Giovanni, Eiben, A. E., Ascheid, Gerd

In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycl

Externí odkaz: http://arxiv.org/abs/2007.04725

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