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simon wiedemann
simon wiedemann
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Robust and communication-efficient federated learning from non-iid data
F Sattler, S Wiedemann, KR Müller, W Samek
IEEE transactions on neural networks and learning systems 31 (9), 3400-3413, 2019
5382019
Sparse binary compression: Towards distributed deep learning with minimal communication
F Sattler, S Wiedemann, KR Müller, W Samek
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
1062019
Pruning by explaining: A novel criterion for deep neural network pruning
SK Yeom, P Seegerer, S Lapuschkin, A Binder, S Wiedemann, KR Müller, ...
Pattern Recognition 115, 107899, 2021
492021
Deepcabac: A universal compression algorithm for deep neural networks
S Wiedemann, H Kirchhoffer, S Matlage, P Haase, A Marban, T Marinč, ...
IEEE Journal of Selected Topics in Signal Processing 14 (4), 700-714, 2020
422020
Compact and computationally efficient representation of deep neural networks
S Wiedemann, KR Müller, W Samek
IEEE transactions on neural networks and learning systems 31 (3), 772-785, 2019
422019
Entropy-constrained training of deep neural networks
S Wiedemann, A Marban, KR Müller, W Samek
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
252019
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
S Wiedemann, H Kirchhoffer, S Matlage, P Haase, A Marban, T Marinc, ...
arXiv preprint arXiv:1905.08318, 2019
122019
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
S Wiedemann, T Mehari, K Kepp, W Samek
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
72020
Dependent scalar quantization for neural network compression
P Haase, H Schwarz, H Kirchhoffer, S Wiedemann, T Marinc, A Marban, ...
2020 IEEE International Conference on Image Processing (ICIP), 36-40, 2020
62020
Learning sparse & ternary neural networks with entropy-constrained trained ternarization (ec2t)
A Marban, D Becking, S Wiedemann, W Samek
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
62020
Deepcabac: Plug & play compression of neural network weights and weight updates
D Neumann, F Sattler, H Kirchhoffer, S Wiedemann, K Müller, H Schwarz, ...
2020 IEEE International Conference on Image Processing (ICIP), 21-25, 2020
52020
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons
S Wiedemann, S Shivapakash, D Becking, P Wiedemann, W Samek, ...
IEEE Open Journal of Circuits and Systems 2, 407-419, 2021
42021
Pruning and/or quantizing machine learning predictors
W Samek, S Lapuschkin, S Wiedemann, P Seegerer, Y Seul-Ki, K Mueller, ...
US Patent App. 17/556,657, 2022
2022
Methods and apparatuses for compressing parameters of neural networks
P Haase, AM GONZALEZ, H Kirchhoffer, T Marinc, D Marpe, S Matlage, ...
US Patent App. 17/478,676, 2022
2022
Compact and efficient representations of deep neural networks
S Wiedemann
2022
Concepts for distributed learning of neural networks and/or transmission of parameterization updates therefor
W Samek, S Wiedemann, F Sattler, KR Müller, T Wiegand
US Patent App. 17/096,887, 2021
2021
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