Bernd Scholz-Reiter

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 5 citations into it

Whom Bernd Scholz-Reiter trusts overall

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

1. Geoffrey E. Hinton
2. Rob J Hyndman
3. David H. Hubel
4. Nirbhar Neogi
5. Yann LeCun
6. George Athanasopoulos
7. David E. Goldberg

Who trusts Bernd Scholz-Reiter overall

1. Octavio Loyola-González
2. Liquan Zhao
3. Jun Zhu
4. Tian Wang

Papers from Bernd Scholz-Reiter's perspective

Ordered by how strongly the researchers Bernd Scholz-Reiter trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
3. Rectified Linear Units Improve Restricted Boltzmann Machines
4. Dropout: a simple way to prevent neural networks from overfitting
5. ImageNet classification with deep convolutional neural networks
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection (2016) — 530 citations
• A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands (2013) — 14 citations