Synho Do

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it
• Artificial Intelligence — 35 citations into it
• Model Architectures — 14 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Synho Do trusts overall

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

1. Karen Simonyan
2. Matthew D. Zeiler
3. Yann LeCun

Papers from Synho Do's perspective

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

1. Gradient-based learning applied to document recognition
2. ImageNet Large Scale Visual Recognition Challenge
3. Going deeper with convolutions
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Visualizing and Understanding Convolutional Networks
7. Caffe: Convolutional Architecture for Fast Feature Embedding
8. Learning Multiple Layers of Features from Tiny Images
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• How much data is needed to train a medical image deep learning system to achieve necessary high accuracy (2015) — 385 citations
• An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets (2018) — 384 citations
• Fully Automated Deep Learning System for Bone Age Assessment (2017) — 426 citations