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ORCIDMatthias Kissel
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2020 – today
- 2024
[b1]Matthias Kissel:
Neural Networks with Sequentially Semiseparable Weight Matrices. Technical University of Munich, Germany, 2024- 2023
- [j1]Matthias Kissel
, Klaus Diepold:
Structured Matrices and Their Application in Neural Networks: A Survey. New Gener. Comput. 41(3): 697-722 (2023) - [c9]Luca Sacchetto, Mathias Korte, Sven Gronauer, Matthias Kissel, Klaus Diepold:
Offline Reinforcement Learning for Quadrotor Control: Overcoming the Ground Effect. IROS 2023: 7539-7544 - [c8]Matthias Kissel, Klaus Diepold:
Neural Networks Comprising Sequentially Semiseparable Matrices with One Dimensional State Variable are Universal Approximators. PKDD/ECML Workshops (4) 2023: 115-125 - 2022
- [c7]Matthias Kissel, Martin Gottwald, Biljana Gjeroska, Philipp Paukner, Klaus Diepold:
Backpropagation Through States: Training Neural Networks with Sequentially Semiseparable Weight Matrices. EPIA 2022: 476-487 - [c6]Matthias Kissel, Sven Gronauer, Mathias Korte, Luca Sacchetto, Klaus Diepold:
Exploiting Structures in Weight Matrices for Efficient Real-Time Drone Control with Neural Networks. EPIA 2022: 525-536 - [c5]Matthias Kissel, Klaus Diepold:
Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices. ESANN 2022 - [c4]Matthias Kissel, Klaus Diepold:
Convolutional Neural Networks with analytically determined Filters. IJCNN 2022: 1-7 - [c3]Sven Gronauer, Matthias Kissel, Luca Sacchetto, Mathias Korte, Klaus Diepold:
Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors. IROS 2022: 10170-10176 - [i1]Sven Gronauer, Matthias Kissel, Luca Sacchetto, Mathias Korte, Klaus Diepold:
Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors. CoRR abs/2201.01369 (2022) - 2020
- [c2]Matthias Kissel, Martin Gottwald, Klaus Diepold:
Neural Network Training with Safe Regularization in the Null Space of Batch Activations. ICANN (2) 2020: 217-228
2010 – 2019
- 2019
- [c1]Matthias Kissel, Klaus Diepold:
Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks. ECML/PKDD (2) 2019: 399-414
Coauthor Index
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