Simon Wiedemann

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 27

Top Topics

Backpropagation Algorithm

Policy Iteration

Arithmetic Coding

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Simon Wiedemann, Reinhard Heckel
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography. CoRR (2023)
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)
Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian Mendl
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. CoRR (2022)
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 (2021)
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 (2021)
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)
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) (2020)
Simon Wiedemann, Temesgen Mehari, Kevin Kepp, Wojciech Samek
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training. CoRR (2020)
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)
Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 31 (3) (2020)
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)
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 (2020)
Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CVPR Workshops (2020)
Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CoRR (2020)
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) (2020)
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 (2019)
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Robust and Communication-Efficient Federated Learning from Non-IID Data. CoRR (2019)
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication. IJCNN (2019)
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 (2019)
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 (2019)
Simon Wiedemann, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek
Entropy-Constrained Training of Deep Neural Networks. IJCNN (2019)
Simon Wiedemann, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek
Entropy-Constrained Training of Deep Neural Networks. CoRR (2018)
Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Compact and Computationally Efficient Representation of Deep Neural Networks. CoRR (2018)
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication. CoRR (2018)
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) (2017)
Simon Wiedemann, Ralph M. Kennel
Encoderless self-commissioning and identification of synchronous reluctance machines at standstill. ISIE (2017)
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)