Sae-Young Chung

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 156 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Reinforcement Learning — 17 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Sae-Young Chung trusts overall

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

1. Diederik P. Kingma
2. John Schulman
3. Geoffrey E. Hinton
4. E. Todorov
5. Pieter Abbeel
6. Alex Krizhevsky
7. Sergey Levine
8. Yoshua Bengio
9. J. Urgen Schmidhuber
10. Richard S. Sutton

Who trusts Sae-Young Chung overall

1. Khimya Khetarpal

Papers from Sae-Young Chung's perspective

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

Their papers

• Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture (2021) — 22 citations