Johannes Günther

Orcid: 0000-0003-4926-312X

Affiliations:
  • University of Alberta, Edmonton, Canada
  • Technical University Munich, Germany (PhD 2018)


According to our database1, Johannes Günther authored at least 16 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
General value functions for fault detection in multivariate time series data.
Frontiers Robotics AI, 2024

Explainability of deep reinforcement learning algorithms in robotic domains by using Layer-wise Relevance Propagation.
Eng. Appl. Artif. Intell., 2024

2023
Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022
Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge.
Frontiers Artif. Intell., 2022

Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have.
CoRR, 2022

Prototyping three key properties of specific curiosity in computational reinforcement learning.
CoRR, 2022

Affordance as general value function: a computational model.
Adapt. Behav., 2022

Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden.
Proceedings of the International Conference on Rehabilitation Robotics, 2022

What Should I Know? Using Meta-Gradient Descent for Predictive Feature Discovery in a Single Stream of Experience.
Proceedings of the Conference on Lifelong Learning Agents, 2022

2021
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making.
CoRR, 2021

2020
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures.
Frontiers Robotics AI, 2020

Gamma-Nets: Generalizing Value Estimation over Timescale.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison.
CoRR, 2019

Detecting the Onset of Machine Failure Using Anomaly Detection Methods.
Proceedings of the Big Data Analytics and Knowledge Discovery, 2019

Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Machine intelligence for adaptable closed loop and open loop production engineering systems.
PhD thesis, 2018


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