Hongda Wang

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it
• Artificial Intelligence — 12 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Hongda Wang trusts overall

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

1. Kaiming He
2. X. Zhang
3. Zhou Wang
4. Diederik P. Kingma
5. Shaoqing Ren
6. Geoffrey E. Hinton
7. Yair Rivenson
8. Alex Krizhevsky

Who trusts Hongda Wang overall

1. Samuel Budd

Papers from Hongda Wang's perspective

Ordered by how strongly the researchers Hongda Wang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Deep learning enables cross-modality super-resolution in fluorescence microscopy (2018) — 713 citations
• Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning (2018) — 612 citations
• Deep Learning Microscopy: Enhancing Resolution, Field-of-View and Depth-of-Field of Optical Microscopy Images Using Neural Networks (2017) — 403 citations