Christopher A. Metzler

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 3 papers; trusts 184 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it; #5,897 of 21,575 by trust here, #10,010 by citations
• Computational Mechanics — 85 citations into it; #1,744 of 10,001 by trust here, #2,309 by citations
• Biomedical Engineering — 30 citations into it
• Media Technology — 27 citations into it
• Signal Processing — 24 citations into it; #2,081 of 7,572 by trust here, #3,428 by citations
• Artificial Intelligence — 18 citations into it
• Computer Networks and Communications — 12 citations into it
• Generative Models — 12 citations into it

Whom Christopher A. Metzler trusts overall

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

1. David L. Donoho
2. E.J. Candes
3. Joel A. Tropp
4. Richard Baraniuk
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Kaiming He
9. Yann LeCun
10. Arian Maleki

Who trusts Christopher A. Metzler overall

1. Chao-Kai Wen
2. Ali Mousavi
3. Hengtao He
4. Yan Yang
5. Shi Jin
6. Jian Zhang
7. Richard Baraniuk
8. Vishal Monga
9. Jun Shu
10. Ernest K. Ryu

Papers from Christopher A. Metzler's perspective

Ordered by how strongly the researchers Christopher A. Metzler 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• From Denoising to Compressed Sensing (2014) — 672 citations
• Learned D-AMP: Principled Neural Network based Compressive Image Recovery (2017) — 321 citations
• Deep Learning Techniques for Inverse Problems in Imaging (2020) — 670 citations