Hyeong Gwon Hong

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 222 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 14 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Hyeong Gwon Hong trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. James Martens
7. Karen Simonyan
8. Sergey Ioffe
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Hyeong Gwon Hong overall

1. Da-Wei Zhou
2. Liyuan Wang
3. Yajing Kong
4. Hyounguk Shon
5. Prakhar Kaushik

Papers from Hyeong Gwon Hong's perspective

Ordered by how strongly the researchers Hyeong Gwon Hong 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Continual Learning With Extended Kronecker-Factored Approximate Curvature (2020) — 72 citations
• Residual Continual Learning (2020) — 25 citations