Baoyuan Liu

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 309 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #7,283 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 25 citations into it
• Efficiency & Compression — 22 citations into it; #355 of 1,264 by trust here, #360 by citations
• Model Architectures — 20 citations into it; #728 of 2,278 by trust here, #600 by citations
• Optimization & Training — 9 citations into it
• Computational Mechanics — 6 citations into it; #2,681 of 10,001 by trust here, #1,031 by citations

Whom Baoyuan Liu trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Ross B. Girshick
6. Ilya Sutskever
7. Yann LeCun
8. X. Zhang
9. Sergey Ioffe
10. Jia Deng

Who trusts Baoyuan Liu overall

1. Wujie Wen
2. Rahaf Aljundi
3. José Manuel Álvarez
4. Haichuan Yang
5. Min Wang
6. Huanrui Yang
7. Wu Zheng
8. Yunhe Wang
9. Weiyang Liu
10. Peisong Wang

Papers from Baoyuan Liu's perspective

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

Their papers

• Sparse Convolutional Neural Networks (2015) — 924 citations
• Factorized Convolutional Neural Networks (2016) — 188 citations
• Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure (2016) — 53 citations