A. King

Computer Vision and Pattern Recognition · with Clough · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it
• Model Architectures — 9 citations into it
• Biomedical Engineering — 6 citations into it
• Media Technology — 5 citations into it

Whom A. King trusts overall

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

1. Karen Simonyan

Papers from A. King's perspective

Ordered by how strongly the researchers A. King trusts endorse each paper. Same corpus, a different order for every researcher.

1. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
2. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
3. Fully convolutional networks for semantic segmentation
4. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
5. ImageNet classification with deep convolutional neural networks
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017) — 431 citations
• Global and Local Interpretability for Cardiac MRI Classification (2019) — 70 citations
• A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology (2019) — 309 citations