Johannes Günther

发表

Automated Optimization of Dynamic Neural Network Structure using Genetic Algorithms

Klaus Diepold, Christiane Sandner, Johannes Günther, 2017 .

Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Patrick M. Pilarski, Klaus Diepold, Johannes Günther, 2016 .

Scalable Ray Tracing Using the Distributed FrameBuffer

Valerio Pascucci, Ingo Wald, Carson Brownlee, 2019, Comput. Graph. Forum.

Machine intelligence for adaptable closed loop and open loop production engineering systems

Johannes Günther, 2018 .

Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm

Patrick M. Pilarski, Johannes Günther, Michael Rory Dawson, 2019, AAMAS.

Neural Networks for fast sensor data processing in Laser Welding

Klaus Diepold, Johannes Günther, Hao Shen, 2014 .

General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison pdf

Patrick M. Pilarski, Klaus Diepold, Elias Reichensdörfer, 2019, ArXiv.

First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning

Patrick M. Pilarski, Klaus Diepold, Hao Shen, 2014 .

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures

Patrick M. Pilarski, Johannes Günther, Michael Rory Dawson, 2019, Frontiers in Robotics and AI.

Detecting the Onset of Machine Failure Using Anomaly Detection Methods

Osmar R. Zaïane, Johannes Günther, Mohammad Riazi, 2019, DaWaK.

Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making pdf

Patrick M. Pilarski, Johannes Günther, Anna Koop, 2021, ArXiv.