Hans Peter Graf

Computer Vision and Pattern Recognition · 1988–2017

A researcher in the Science corpus: 6 papers; trusts 268 researchers and is trusted by 297 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it; #2,378 of 21,575 by trust here, #2,024 by citations
• Artificial Intelligence — 47 citations into it; #4,033 of 35,345 by trust here, #2,869 by citations
• Efficiency & Compression — 21 citations into it; #137 of 1,264 by trust here, #117 by citations
• Model Architectures — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Optimization & Training — 10 citations into it; #1,238 of 2,601 by trust here, #2,484 by citations
• Natural Language Processing — 9 citations into it

Whom Hans Peter Graf trusts overall

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

1. Ronan Collobert
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Bernhard E. Boser
8. Christian Szegedy
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Hans Peter Graf overall

1. Matthieu Courbariaux
2. Yoshua Bengio
3. Yann LeCun
4. Fu Jie Huang
5. Yi Yang
6. Bryan Catanzaro
7. Yang He
8. Xiaojun Chang
9. Artur Andrzejak
10. Ş. Ertekin

Papers from Hans Peter Graf's perspective

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

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

Their papers

• Pruning Filters for Efficient ConvNets (2016) — 4,125 citations
• Attend and Interact: Higher-Order Object Interactions for Video Understanding (2017) — 149 citations
• Parallel Support Vector Machines: The Cascade SVM (2004) — 507 citations
• Backpropagation without Multiplication (1993)
• Handwritten digit recognition: applications of neural network chips and automatic learning (1989)
• Neural Network Recognizer for Hand-Written Zip Code Digits (1988)