Simon Wiedemann

According to our database, Simon Wiedemann authored at least 21 papers between 2017 and 2023.

Collaborative distances:

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

2023

Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning.

Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl

Trans. Mach. Learn. Res., 2023

A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography.

Simon Wiedemann, Reinhard Heckel

CoRR, 2023

2022

Compact and efficient representations of deep neural networks.

Simon Wiedemann

PhD thesis, 2022

2021

Pruning by explaining: A novel criterion for deep neural network pruning.

Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

Pattern Recognit., 2021

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons.

Simon Wiedemann, Suhas Shivapakash, Daniel Becking, Pablo Wiedemann, Wojciech Samek, Friedel Gerfers, Thomas Wiegand

IEEE Open J. Circuits Syst., 2021

2020

Compact and Computationally Efficient Representation of Deep Neural Networks.

Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

IEEE Trans. Neural Networks Learn. Syst., 2020

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data.

Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

IEEE Trans. Neural Networks Learn. Syst., 2020

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks.

Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán, Talmaj Marinc, David Neumann, Tung Nguyen, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek

IEEE J. Sel. Top. Signal Process., 2020

Deepcabac: Plug & Play Compression of Neural Network Weights and Weight Updates.

David Neumann, Felix Sattler, Heiner Kirchhoffer, Simon Wiedemann, Karsten Müller, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek

Proceedings of the IEEE International Conference on Image Processing, 2020

Dependent Scalar Quantization For Neural Network Compression.

Paul Haase, Heiko Schwarz, Heiner Kirchhoffer, Simon Wiedemann, Talmaj Marinc, Arturo Marbán, Karsten Müller, Wojciech Samek, Detlev Marpe, Thomas Wiegand

Proceedings of the IEEE International Conference on Image Processing, 2020

Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training.

Simon Wiedemann, Temesgen Mehari, Kevin Kepp, Wojciech Samek

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T).

Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning.

Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

CoRR, 2019

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks.

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

CoRR, 2019

DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression.

Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek

CoRR, 2019

Robust and Communication-Efficient Federated Learning from Non-IID Data.

Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

CoRR, 2019

Entropy-Constrained Training of Deep Neural Networks.

Simon Wiedemann, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek

Proceedings of the International Joint Conference on Neural Networks, 2019

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication.

Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

Proceedings of the International Joint Conference on Neural Networks, 2019

2017

A discrete-time queueing network approach to performance evaluation of autonomous vehicle storage and retrieval systems.

Martin Epp, Simon Wiedemann, Kai Furmans

Int. J. Prod. Res., 2017

Encoderless self-commissioning and identification of synchronous reluctance machines at standstill.

Simon Wiedemann, Ralph M. Kennel

Proceedings of the 26th IEEE International Symposium on Industrial Electronics, 2017

Comparison between linear- and nonlinear-feedback control for a synchronous reluctance machine.

Anton H. Tamas, Claudia S. Martis, Simon Wiedemann, Ralph M. Kennel

Proceedings of the 26th IEEE International Symposium on Industrial Electronics, 2017