Andreas M. Lehrmann

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 3 papers; trusts 248 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #9,298 of 21,575 by trust here, #8,021 by citations
• Artificial Intelligence — 30 citations into it; #28,299 of 35,345 by trust here, #28,481 by citations
• Multimodal Learning — 18 citations into it; #735 of 1,211 by trust here, #846 by citations
• Natural Language Processing — 18 citations into it
• 3D Vision & Pose — 15 citations into it
• Aerospace Engineering — 12 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Andreas M. Lehrmann trusts overall

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

1. Diederik P. Kingma
2. Neil D. Lawrence
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Alex Krizhevsky
7. Jimmy Ba
8. Kaiming He
9. Yann LeCun
10. Ilya Sutskever

Who trusts Andreas M. Lehrmann overall

1. Dushyant Mehta
2. Marta Sanzari
3. Helge Rhodin
4. Satwik Kottur
5. Wenzheng Chen
6. Hanwang Zhang
7. Tianhao Yang
8. Yulei Niu
9. Valsamis Ntouskos
10. Dan Casas

Papers from Andreas M. Lehrmann's perspective

Ordered by how strongly the researchers Andreas M. Lehrmann 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Visual Reference Resolution using Attention Memory for Visual Dialog (2017) — 125 citations
• A Non-parametric Bayesian Network Prior of Human Pose (2013) — 50 citations
• Neural volumes (2019) — 338 citations