Baosheng Yu

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 770 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 251 citations into it; #17,814 of 35,345 by trust here, #13,163 by citations
• Computer Vision and Pattern Recognition — 230 citations into it; #11,231 of 21,575 by trust here, #9,079 by citations
• Efficiency & Compression — 69 citations into it; #654 of 1,264 by trust here, #705 by citations
• Signal Processing — 25 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Natural Language Processing — 15 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 11 citations into it

Whom Baosheng Yu trusts overall

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

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

Who trusts Baosheng Yu overall

1. Elias Frantar
2. Julian Togelius
3. Mang Wang
4. Xiuyi Chen
5. Patrick M. Haluptzok
6. J. Ainslie
7. Michiel de Jong
8. Liyuan Wang
9. Yufei Xu
10. Tal Ridnik

Papers from Baosheng Yu's perspective

Ordered by how strongly the researchers Baosheng Yu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Learning Multiple Layers of Features from Tiny Images
10. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers

• Knowledge Distillation: A Survey (2020) — 4,395 citations