Jo Schlemper

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 166 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it
• Artificial Intelligence — 46 citations into it
• Radiology, Nuclear Medicine and Imaging — 42 citations into it; #1,047 of 3,265 by trust here, #435 by citations
• Model Architectures — 34 citations into it
• Computational Mechanics — 20 citations into it
• Biomedical Engineering — 18 citations into it; #9,109 of 10,734 by trust here, #6,455 by citations
• Media Technology — 14 citations into it
• Optimization & Training — 8 citations into it

Whom Jo Schlemper trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Radford M. Neal
5. X. Zhang
6. Yoshua Bengio
7. Alex Krizhevsky
8. Karen Simonyan
9. Christian Szegedy
10. Shaoqing Ren

Who trusts Jo Schlemper overall

1. Arvid Lundervold
2. Hemant Kumar Aggarwal
3. Jun Shu
4. Yundong Zhang
5. Jieneng Chen
6. Kaiwen Zhang
7. Nahian Alam Siddique
8. Merry P. Mani
9. Zongben Xu
10. Chenxin Li

Papers from Jo Schlemper's perspective

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

Their papers

• Attention U-Net: Learning Where to Look for the Pancreas (2018) — 7,627 citations
• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018) — 1,840 citations
• A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2017) — 1,272 citations
• A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction (2017) — 370 citations
• Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction (2017) — 569 citations