Jianhua Yao

Computer Vision and Pattern Recognition · 2005–2019

A researcher in the Science corpus: 8 papers; trusts 481 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #16,187 of 21,575 by trust here, #2,857 by citations
• Artificial Intelligence — 108 citations into it; #28,011 of 35,345 by trust here, #8,619 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Model Architectures — 26 citations into it
• Natural Language Processing — 19 citations into it
• Media Technology — 14 citations into it
• Multimodal Learning — 13 citations into it
• Optimization & Training — 9 citations into it

Whom Jianhua Yao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Ilya Sutskever
5. Yann LeCun
6. Yoshua Bengio
7. Andrew Zisserman
8. Diederik P. Kingma
9. Kaiming He
10. Ross B. Girshick

Who trusts Jianhua Yao overall

1. Maciej A. Mazurowski
2. Le Lu
3. B. van Ginneken

Papers from Jianhua Yao's perspective

Ordered by how strongly the researchers Jianhua Yao 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2016) — 5,088 citations
• Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation (2016) — 394 citations
• Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017) — 41 citations
• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019) — 70 citations
• DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017) — 431 citations
• Deep convolutional networks for automated detection of posterior-element fractures on spine CT (2016) — 58 citations
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015) — 585 citations
• Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. (2005) — 314 citations