Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks. - Matthias Kissel; Klaus Diepold (ECML/PKDD (2), 2019)

Neural Network Training with Safe Regularization in the Null Space of Batch Activations. - Matthias Kissel; Martin Gottwald; Klaus Diepold (ICANN (2), 2020)

Backpropagation Through States: Training Neural Networks with Sequentially Semiseparable Weight Matrices. - Matthias Kissel; Martin Gottwald; Biljana Gjeroska; Philipp Paukner; Klaus Diepold (EPIA, 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 (EPIA, 2022)

Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices. - Matthias Kissel; Klaus Diepold (ESANN, 2022)

Convolutional Neural Networks with analytically determined Filters. - Matthias Kissel; Klaus Diepold (IJCNN, 2022)

Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors. - Sven Gronauer; Matthias Kissel; Luca Sacchetto; Mathias Korte; Klaus Diepold (IROS, 2022)

Offline Reinforcement Learning for Quadrotor Control: Overcoming the Ground Effect. - Luca Sacchetto; Mathias Korte; Sven Gronauer; Matthias Kissel; Klaus Diepold (IROS, 2023)

Neural Networks Comprising Sequentially Semiseparable Matrices with One Dimensional State Variable are Universal Approximators. - Matthias Kissel; Klaus Diepold (PKDD/ECML Workshops (4), 2023)

Structured Matrices and Their Application in Neural Networks: A Survey. - Matthias Kissel; Klaus Diepold (New Gener. Comput., 2023)

Neural Networks with Sequentially Semiseparable Weight Matrices. - Matthias Kissel (2024)