Jiahua Dong

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 6 papers; trusts 400 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #19,583 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 110 citations into it; #12,274 of 21,575 by trust here, #13,269 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Meta & Continual Learning — 21 citations into it
• Safety, Robustness & Interpretability — 19 citations into it
• Optimization & Training — 16 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Generative Models — 9 citations into it

Whom Jiahua Dong trusts overall

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

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

Who trusts Jiahua Dong overall

1. Yahao Liu
2. Tong Chu
3. Zhen Fang
4. Da-Wei Zhou
5. Jinhong Deng
6. Lixu Wang
7. Jie M. Zhang
8. Qiwen Wang
9. Wen Li
10. Jie Lu

Papers from Jiahua Dong's perspective

Ordered by how strongly the researchers Jiahua Dong 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Data Poisoning Attacks on Federated Machine Learning (2020) — 293 citations
• Is Out-of-Distribution Detection Learnable? (2022) — 181 citations
• Federated Class-Incremental Learning (2022) — 299 citations
• Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation (2021) — 153 citations
• What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation (2020) — 149 citations
• I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting (2020) — 39 citations