Surat Teerapittayanon

Computer Vision and Pattern Recognition · 2009–2017

A researcher in the Science corpus: 3 papers; trusts 133 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it; #3,965 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 24 citations into it; #4,456 of 35,345 by trust here, #6,614 by citations
• Optimization & Training — 12 citations into it
• Efficiency & Compression — 11 citations into it; #199 of 1,264 by trust here, #461 by citations
• Model Architectures — 10 citations into it
• Molecular Biology — 5 citations into it

Whom Surat Teerapittayanon trusts overall

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

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

Who trusts Surat Teerapittayanon overall

1. Ji Xin
2. Zhi Zhou
3. En Li
4. Yulin Wang
5. Simone Scardapane
6. Liekang Zeng
7. Xu Chen
8. M. G. Sarwar Murshed
9. Zuxuan Wu
10. Charles Herrmann

Papers from Surat Teerapittayanon's perspective

Ordered by how strongly the researchers Surat Teerapittayanon 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• BranchyNet: Fast inference via early exiting from deep neural networks (2016) — 1,542 citations
• Rapid prototyping of 3D DNA-origami shapes with caDNAno (2009) — 1,160 citations
• Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices (2017) — 800 citations