Flood Sung

Artificial Intelligence · 2017

A researcher in the Science corpus: 4 papers; trusts 217 researchers and is trusted by 221 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #7,025 of 35,345 by trust here, #2,723 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #2,602 of 21,575 by trust here, #2,273 by citations
• Reinforcement Learning — 31 citations into it; #2,053 of 2,242 by trust here, #1,280 by citations
• Representation & Self-Supervised Learning — 31 citations into it; #1,156 of 3,145 by trust here, #420 by citations
• Natural Language Processing — 14 citations into it

Whom Flood Sung trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Oriol Vinyals
7. Yann LeCun
8. Richard S. Sutton
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Flood Sung overall

1. Han-Jia Ye
2. Chi Zhang
3. Qianru Sun
4. Yu-Xiong Wang
5. Tristan Sylvain
6. Yaoyao Liu
7. Wei-Hong Li
8. Wei-Lun Chao
9. Yixiong Zou
10. De-chuan Zhan

Papers from Flood Sung's perspective

Ordered by how strongly the researchers Flood Sung 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning to Compare: Relation Network for Few-Shot Learning (2017) — 4,711 citations
• Learning to Learn: Meta-Critic Networks for Sample Efficient Learning (2017) — 140 citations
• Actor-Critic Sequence Training for Image Captioning (2017) — 116 citations
• Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions (2017) — 51 citations