Tian Guo

Computer Vision and Pattern Recognition · with Dai · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 152 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it
• Artificial Intelligence — 19 citations into it
• Efficiency & Compression — 13 citations into it
• Model Architectures — 11 citations into it
• Computer Networks and Communications — 6 citations into it; #3,445 of 9,886 by trust here, #8,543 by citations
• Electrical and Electronic Engineering — 5 citations into it; #4,194 of 16,539 by trust here, #15,217 by citations

Whom Tian Guo trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Min Lin
6. Yoshua Bengio
7. Kaiming He
8. Forrest N. Iandola
9. L. Eon Bottou
10. Ronald J. Williams

Who trusts Tian Guo overall

1. Young Geun Kim
2. Carole-Jean Wu
3. Yoshitomo Matsubara
4. James Smith

Papers from Tian Guo's perspective

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

Their papers

• Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference (2017) — 71 citations
• EPNet: Learning to Exit with Flexible Multi-Branch Network (2020) — 61 citations