Sungsoo Ahn

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 132 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Reinforcement Learning — 42 citations into it
• Efficiency & Compression — 29 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Game & Decision Theory — 6 citations into it

Whom Sungsoo Ahn trusts overall

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

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

Who trusts Sungsoo Ahn overall

1. Lin Wang
2. Paul Micaelli
3. Yonglong Tian
4. Rafael Müller
5. Yoojin Choi
6. Defang Chen
7. Guodong Xu
8. K. J. Joseph
9. Ajinkya Tejankar
10. Shipeng Fu

Papers from Sungsoo Ahn's perspective

Ordered by how strongly the researchers Sungsoo Ahn trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Variational Information Distillation for Knowledge Transfer (2019) — 763 citations
• Learning from Failure: De-biasing Classifier from Biased Classifier (2020) — 416 citations
• QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning (2020) — 30 citations
• Layer-adaptive Sparsity for the Magnitude-based Pruning (2020) — 354 citations
• QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning (2020)