Bo Zhao

Computer Vision and Pattern Recognition · with Bilen · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 1,008 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 180 citations into it; #7,716 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 172 citations into it; #8,897 of 35,345 by trust here, #12,974 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Meta & Continual Learning — 40 citations into it
• Optimization & Training — 38 citations into it
• Model Architectures — 30 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Generative Models — 21 citations into it

Whom Bo Zhao trusts overall

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

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

Who trusts Bo Zhao overall

1. Hakan Bilen
2. Timothy Nguyen
3. George Cazenavette
4. Lingjuan Lyu
5. Xuefei Yin
6. Roman Novak
7. Zhicheng Sun
8. Tongzhou Wang
9. Jonas Geiping
10. Han Yu

Papers from Bo Zhao's perspective

Ordered by how strongly the researchers Bo Zhao 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Dataset Condensation with Gradient Matching (2020) — 720 citations
• Dataset Condensation with Distribution Matching (2021) — 485 citations
• Continual Representation Learning for Biometric Identification (2020) — 50 citations
• Dataset Condensation with Differentiable Siamese Augmentation (2021) — 410 citations
• iDLG: Improved Deep Leakage from Gradients (2020) — 844 citations
• MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning (2018) — 23 citations