Matthias Kissel

According to our database, Matthias Kissel authored at least 10 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Neural Networks with Sequentially Semiseparable Weight Matrices.

Matthias Kissel

PhD thesis, 2024

2023

Structured Matrices and Their Application in Neural Networks: A Survey.

Matthias Kissel, Klaus Diepold

New Gener. Comput., September, 2023

Offline Reinforcement Learning for Quadrotor Control: Overcoming the Ground Effect.

Luca Sacchetto, Mathias Korte, Sven Gronauer, Matthias Kissel, Klaus Diepold

IROS, 2023

2022

Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors.

Sven Gronauer, Matthias Kissel, Luca Sacchetto, Mathias Korte, Klaus Diepold

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Convolutional Neural Networks with analytically determined Filters.

Matthias Kissel, Klaus Diepold

Proceedings of the International Joint Conference on Neural Networks, 2022

Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices.

Matthias Kissel, Klaus Diepold

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Exploiting Structures in Weight Matrices for Efficient Real-Time Drone Control with Neural Networks.

Matthias Kissel, Sven Gronauer, Mathias Korte, Luca Sacchetto, Klaus Diepold

Proceedings of the Progress in Artificial Intelligence, 2022

Backpropagation Through States: Training Neural Networks with Sequentially Semiseparable Weight Matrices.

Matthias Kissel, Martin Gottwald, Biljana Gjeroska, Philipp Paukner, Klaus Diepold

Proceedings of the Progress in Artificial Intelligence, 2022

2020

Neural Network Training with Safe Regularization in the Null Space of Batch Activations.

Matthias Kissel, Martin Gottwald, Klaus Diepold

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2019

Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks.

Matthias Kissel, Klaus Diepold

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019