Philip Heng Wai Leong

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 103 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 38 citations into it; #20,590 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 32 citations into it; #33,739 of 35,345 by trust here, #16,471 by citations
• Efficiency & Compression — 20 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Philip Heng Wai Leong trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Kaiming He
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Sergey Ioffe
7. Yann LeCun
8. Matthieu Courbariaux
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Philip Heng Wai Leong overall

1. Sparsh Mittal

Papers from Philip Heng Wai Leong's perspective

Ordered by how strongly the researchers Philip Heng Wai Leong 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. Learning Multiple Layers of Features from Tiny Images
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks (2018) — 142 citations
• FINN: A Framework for Fast, Scalable Binarized Neural Network Inference (2016) — 1,186 citations