Shih-Chieh Chang

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 118 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Reinforcement Learning — 21 citations into it; #2,148 of 2,242 by trust here, #1,609 by citations
• Optimization & Training — 15 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Shih-Chieh Chang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Ross B. Girshick
10. Sergey Ioffe

Who trusts Shih-Chieh Chang overall

1. Zhisheng Zhong

Papers from Shih-Chieh Chang's perspective

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

Their papers

• Task Transfer by Preference-Based Cost Learning (2018) — 56 citations
• A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing (2017) — 44 citations
• Remix: Rebalanced Mixup (2020) — 304 citations
• MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (2018) — 149 citations