Steven W. Su

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 236 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it
• Computer Vision and Pattern Recognition — 105 citations into it
• Neural Architecture Search & AutoML — 49 citations into it
• Model Architectures — 18 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Management Science and Operations Research — 7 citations into it
• Meta & Continual Learning — 7 citations into it

Whom Steven W. Su trusts overall

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

1. Yoshua Bengio
2. Xuanyi Dong
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Diederik P. Kingma
7. Karen Simonyan
8. Quoc V. Le
9. Yann LeCun
10. Ilya Sutskever

Papers from Steven W. Su's perspective

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

Their papers

• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021) — 85 citations
• One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (2020) — 81 citations
• Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization (2020) — 89 citations