Simon Wiedemann

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 158 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #14,713 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #10,120 of 21,575 by trust here, #13,542 by citations
• Efficiency & Compression — 45 citations into it
• Optimization & Training — 41 citations into it
• Model Architectures — 13 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• ML Systems & Hardware — 10 citations into it
• Signal Processing — 10 citations into it

Whom Simon Wiedemann trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Song Han
7. Ilya Sutskever
8. Kaiming He
9. Karen Simonyan
10. Wojciech Samek

Who trusts Simon Wiedemann overall

1. Mohammad Mohammadi Amiri
2. Felix Sattler
3. Deniz Gündüz
4. K. Müller
5. Dinh C. Nguyen
6. Wojciech Samek
7. Jianyu Wang
8. Lumin Liu
9. Jinhyun Ahn
10. Qinbin Li

Papers from Simon Wiedemann's perspective

Ordered by how strongly the researchers Simon Wiedemann trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Compact and Computationally Efficient Representation of Deep Neural Networks (2018) — 77 citations
• Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data (2019) — 1,679 citations
• Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (2018) — 243 citations
• Entropy-Constrained Training of Deep Neural Networks (2018) — 31 citations