Bo Han

Artificial Intelligence · Neural Information Processing Systems · 2016–2022

A researcher in the Science corpus: 11 papers; trusts 356 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 290 citations into it; #9,601 of 35,345 by trust here, #5,084 by citations
• Computer Vision and Pattern Recognition — 148 citations into it
• Optimization & Training — 89 citations into it; #2,347 of 2,601 by trust here, #787 by citations
• Safety, Robustness & Interpretability — 84 citations into it
• Statistical Learning Theory — 38 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Computational Mechanics — 21 citations into it

Whom Bo Han trusts overall

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

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

Who trusts Bo Han overall

1. Hwanjun Song
2. Junnan Li
3. Xiaobo Xia
4. Chen Li
5. Yueming Lyu
6. Haiyang Zhang
7. Elad Amrani
8. Sheng Liu
9. Minseok Kim
10. Pengfei Chen

Papers from Bo Han's perspective

Ordered by how strongly the researchers Bo Han 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Co-teaching: Robust training of deep neural networks with extremely noisy labels (2018) — 2,654 citations
• Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (2020) — 456 citations
• How does Disagreement Help Generalization against Label Corruption? (2019) — 972 citations
• Are Anchor Points Really Indispensable in Label-Noise Learning? (2019) — 461 citations
• Is Out-of-Distribution Detection Learnable? (2022) — 181 citations
• Understanding and Improving Early Stopping for Learning with Noisy Labels (2021) — 298 citations
• Robust early-learning: Hindering the memorization of noisy labels (2021) — 338 citations
• Geometry-aware Instance-reweighted Adversarial Training (2020) — 318 citations
• Part-dependent Label Noise: Towards Instance-dependent Label Noise (2020) — 313 citations
• Privacy-Preserving Stochastic Gradual Learning (2018) — 9 citations
• On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent (2016) — 22 citations