Han Zhao

Artificial Intelligence · with Gordon · 2001–2021

A researcher in the Science corpus: 12 papers; trusts 511 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 304 citations into it; #10,168 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 220 citations into it
• Representation & Self-Supervised Learning — 146 citations into it
• Optimization & Training — 45 citations into it
• Meta & Continual Learning — 36 citations into it; #686 of 1,041 by trust here, #711 by citations
• Natural Language Processing — 30 citations into it; #2,561 of 5,688 by trust here, #2,955 by citations
• Generative Models — 28 citations into it
• Model Architectures — 28 citations into it

Whom Han Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. L. Eon Bottou
10. Radford M. Neal

Who trusts Han Zhao overall

1. Tameem Adel
2. Garrett Wilson
3. Hongyang Gao
4. Gang Liu
5. Mohammad Mahdi Bejani
6. Xinchi Chen
7. Alexis Conneau
8. Junfeng Wen
9. Jihun Choi
10. Tao Lei

Papers from Han Zhao's perspective

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

Their papers

• Self-Adaptive Hierarchical Sentence Model (2015) — 191 citations
• Continual Learning with Adaptive Weights (CLAW) (2019) — 79 citations
• Learning Neural Networks with Adaptive Regularization (2019) — 19 citations
• Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations (2021) — 27 citations
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation (2020) — 338 citations
• On Learning Invariant Representations for Domain Adaptation (2019) — 558 citations
• Adversarial Multiple Source Domain Adaptation (2018) — 619 citations
• Efficient Multitask Feature and Relationship Learning (2017) — 26 citations
• Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption (2017) — 28 citations
• Online Algorithms for Sum-Product Networks with Continuous Variables (2016) — 24 citations
• Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks (2016) — 51 citations
• Weld metal composition change during conduction mode laser welding of aluminum alloy 5182 (2001) — 201 citations