Philip Häusser

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 213 researchers and is trusted by 968 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it; #1,398 of 21,575 by trust here, #1,335 by citations
• Artificial Intelligence — 26 citations into it
• Aerospace Engineering — 12 citations into it
• Optimization & Training — 7 citations into it
• Generative Models — 6 citations into it
• Media Technology — 5 citations into it

Whom Philip Häusser trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Kaiming He
6. Ross B. Girshick
7. Yoshua Bengio
8. Jost Tobias Springenberg
9. Jimmy Ba
10. Christian Szegedy

Who trusts Philip Häusser overall

1. Andrea Vedaldi
2. Yi Zhu
3. Xu Ji
4. Yanbei Chen
5. Andreas Geiger
6. Fatma Güney
7. Adam R. Kosiorek
8. Junhwa Hur
9. Haofei Xu
10. Shenlong Wang

Papers from Philip Häusser's perspective

Ordered by how strongly the researchers Philip Häusser 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• FlowNet: Learning Optical Flow with Convolutional Networks (2015) — 4,664 citations
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015) — 3,047 citations
• Associative Deep Clustering: Training a Classification Network with No Labels (2018) — 132 citations