Uta Büchler

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 408 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 38 citations into it; #10,894 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 24 citations into it; #17,276 of 35,345 by trust here, #23,077 by citations
• Representation & Self-Supervised Learning — 18 citations into it; #1,814 of 3,145 by trust here, #2,516 by citations
• Optimization & Training — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Reinforcement Learning — 5 citations into it

Whom Uta Büchler trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Karen Simonyan
6. Ross B. Girshick
7. Andrew Zisserman
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Uta Büchler overall

1. Longlong Jing
2. Yu Kong
3. Jiangliu Wang
4. Yingli Tian
5. Dejing Xu
6. Yun Raymond Fu
7. Jianbo Jiao
8. Jun Xiao

Papers from Uta Büchler's perspective

Ordered by how strongly the researchers Uta Büchler 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. 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. ImageNet Large Scale Visual Recognition Challenge
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

• Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning (2018) — 121 citations