Xun Jia

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 20 citations into it

Whom Xun Jia trusts overall

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

1. Yann LeCun
2. Lutz Prechelt
3. Geoffrey E. Hinton
4. Evan Shelhamer
5. Yoshua Bengio
6. L. Eon Bottou
7. Alex Krizhevsky
8. Andreas Buja
9. Kaiming He
10. Olaf Ronneberger

Papers from Xun Jia's perspective

Ordered by how strongly the researchers Xun Jia 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. Gradient-based learning applied to document recognition
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Rectified Linear Units Improve Restricted Boltzmann Machines

Their papers

• 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture (2018) — 324 citations
• A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning (2017) — 348 citations