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ORCIDSimon Wiedemann
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2020 – today
- 2023
[j7]Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)- [i12]Simon Wiedemann, Reinhard Heckel:
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography. CoRR abs/2311.05539 (2023) - 2022
- [b1]Simon Wiedemann:
Compact and efficient representations of deep neural networks. Technical University of Berlin, Germany, 2022 - [i11]Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. CoRR abs/2206.04741 (2022) - 2021
- [j6]Simon Wiedemann, Suhas Shivapakash
, Daniel Becking, Pablo Wiedemann, Wojciech Samek, Friedel Gerfers, Thomas Wiegand:
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons. IEEE Open J. Circuits Syst. 2: 407-419 (2021) - [j5]Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognit. 115: 107899 (2021) - 2020
- [j4]Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán, Talmaj Marinc, David Neumann, Tung Nguyen, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek:
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks. IEEE J. Sel. Top. Signal Process. 14(4): 700-714 (2020) - [j3]Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 31(3): 772-785 (2020) - [j2]Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3400-3413 (2020) - [c8]Simon Wiedemann, Temesgen Mehari, Kevin Kepp, Wojciech Samek:
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training. CVPR Workshops 2020: 3096-3104 - [c7]Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek:
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CVPR Workshops 2020: 3105-3113 - [c6]David Neumann, Felix Sattler, Heiner Kirchhoffer, Simon Wiedemann, Karsten Müller, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek:
Deepcabac: Plug & Play Compression of Neural Network Weights and Weight Updates. ICIP 2020: 21-25 - [c5]Paul Haase, Heiko Schwarz, Heiner Kirchhoffer, Simon Wiedemann, Talmaj Marinc, Arturo Marbán, Karsten Müller, Wojciech Samek, Detlev Marpe, Thomas Wiegand:
Dependent Scalar Quantization For Neural Network Compression. ICIP 2020: 36-40 - [i10]Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek:
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CoRR abs/2004.01077 (2020) - [i9]Simon Wiedemann, Temesgen Mehari, Kevin Kepp, Wojciech Samek:
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training. CoRR abs/2004.04729 (2020) - [i8]Simon Wiedemann, Suhas Shivapakash, Pablo Wiedemann, Daniel Becking, Wojciech Samek, Friedel Gerfers, Thomas Wiegand:
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons. CoRR abs/2012.11331 (2020)
2010 – 2019
- 2019
- [c4]Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication. IJCNN 2019: 1-8 - [c3]Simon Wiedemann, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek:
Entropy-Constrained Training of Deep Neural Networks. IJCNN 2019: 1-8 - [i7]Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Robust and Communication-Efficient Federated Learning from Non-IID Data. CoRR abs/1903.02891 (2019) - [i6]Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek:
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression. CoRR abs/1905.08318 (2019) - [i5]Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek:
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks. CoRR abs/1907.11900 (2019) - [i4]Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. CoRR abs/1912.08881 (2019) - 2018
- [i3]Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication. CoRR abs/1805.08768 (2018) - [i2]Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Compact and Computationally Efficient Representation of Deep Neural Networks. CoRR abs/1805.10692 (2018) - [i1]Simon Wiedemann, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek:
Entropy-Constrained Training of Deep Neural Networks. CoRR abs/1812.07520 (2018) - 2017
- [j1]Martin Epp, Simon Wiedemann, Kai Furmans:
A discrete-time queueing network approach to performance evaluation of autonomous vehicle storage and retrieval systems. Int. J. Prod. Res. 55(4): 960-978 (2017) - [c2]Anton H. Tamas, Claudia S. Martis, Simon Wiedemann, Ralph M. Kennel:
Comparison between linear- and nonlinear-feedback control for a synchronous reluctance machine. ISIE 2017: 244-251 - [c1]Simon Wiedemann, Ralph M. Kennel:
Encoderless self-commissioning and identification of synchronous reluctance machines at standstill. ISIE 2017: 296-302
Coauthor Index
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