Michael T. McCann

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 305 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it; #6,368 of 21,575 by trust here, #5,013 by citations
• Computational Mechanics — 34 citations into it
• Radiology, Nuclear Medicine and Imaging — 26 citations into it; #3,135 of 3,265 by trust here, #2,536 by citations
• Media Technology — 24 citations into it; #1,030 of 3,621 by trust here, #505 by citations
• Artificial Intelligence — 22 citations into it
• Biomedical Engineering — 21 citations into it; #10,317 of 10,734 by trust here, #8,278 by citations
• Generative Models — 16 citations into it
• Optimization & Training — 13 citations into it

Whom Michael T. McCann trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Kaiming He
5. Ilya Sutskever
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yunjin Chen
9. Kai Zhang
10. Ross B. Girshick

Who trusts Michael T. McCann overall

1. Jonas Adler
2. Jong Chul Ye
3. Yoseob Han
4. Ozan Öktem
5. Harshit Gupta
6. Ajil Jalal
7. Yan Yang
8. Andreas Hauptmann
9. Arvid Lundervold
10. Vishal Monga

Papers from Michael T. McCann's perspective

Ordered by how strongly the researchers Michael T. McCann 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Deep Convolutional Neural Network for Inverse Problems in Imaging (2016) — 2,439 citations
• CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction (2017) — 397 citations
• Diffusion Posterior Sampling for General Noisy Inverse Problems (2022) — 1,760 citations
• Convolutional Neural Networks for Inverse Problems in Imaging: A Review (2017) — 665 citations