Thomas A. Runkler | DeepAI

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Featured Co-authors

Xue Li
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Haoyu Ren
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Alexander Hentschel
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Stefan Depeweg
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Michel Tokic
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Volkmar Sterzing
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Manuel A. Roehrl
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∙ 07/13/2023

TinyMetaFed: Efficient Federated Meta-Learning for TinyML

The field of Tiny Machine Learning (TinyML) has made substantial advance...

0 Haoyu Ren, et al. ∙

∙ 04/11/2023

TinyReptile: TinyML with Federated Meta-Learning

Tiny machine learning (TinyML) is a rapidly growing field aiming to demo...

0 Haoyu Ren, et al. ∙

∙ 07/18/2022

SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT

Internet of Things (IoT) is transforming the industry by bridging the ga...

0 Haoyu Ren, et al. ∙

∙ 07/20/2020

Interpretable Control by Reinforcement Learning

In this paper, three recently introduced reinforcement learning (RL) met...

0 Daniel Hein, et al. ∙

∙ 05/29/2020

Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics

Identifying accurate dynamic models is required for the simulation and c...

0 Manuel A. Roehrl, et al. ∙

∙ 04/29/2018

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies,...

0 Daniel Hein, et al. ∙

∙ 12/12/2017

Interpretable Policies for Reinforcement Learning by Genetic Programming

The search for interpretable reinforcement learning policies is of high ...

0 Daniel Hein, et al. ∙

∙ 09/27/2017

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel an...

0 Daniel Hein, et al. ∙

∙ 05/20/2017

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introdu...

0 Daniel Hein, et al. ∙

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