Hongjoon Ahn

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 470 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #6,330 of 35,345 by trust here, #7,413 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #4,902 of 21,575 by trust here, #5,333 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Meta & Continual Learning — 39 citations into it; #147 of 1,041 by trust here, #178 by citations
• Optimization & Training — 22 citations into it; #672 of 2,601 by trust here, #692 by citations
• Efficiency & Compression — 19 citations into it
• Reinforcement Learning — 18 citations into it
• Model Architectures — 13 citations into it

Whom Hongjoon Ahn trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Arslan Chaudhry
8. Alex Graves
9. Marc'Aurelio Ranzato
10. Jia Deng

Who trusts Hongjoon Ahn overall

1. Sungmin Cha
2. Gyuhak Kim
3. Zixuan Ke
4. Bing Liu
5. Sudhanshu Mittal
6. Jorge Armando Mendez Mendez
7. Rui Gao
8. Sangwon Jung
9. Liyuan Wang
10. Da-Wei Zhou

Papers from Hongjoon Ahn's perspective

Ordered by how strongly the researchers Hongjoon Ahn 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SS-IL: Separated Softmax for Incremental Learning (2020) — 219 citations
• Uncertainty-based Continual Learning with Adaptive Regularization (2019) — 274 citations
• A Simple Class Decision Balancing for Incremental Learning (2020) — 7 citations
• Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (2020) — 163 citations
• Adaptive Group Sparse Regularization for Continual Learning (2020) — 8 citations