Tristan Yang

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Scaling & Learning Theory — 33 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Optimization & Training — 17 citations into it
• Computational Mechanics — 9 citations into it
• Model Architectures — 6 citations into it

Whom Tristan Yang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Peter L. Bartlett
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Mikhail Belkin
7. X. Zhang
8. Ilya Sutskever
9. Yann LeCun
10. Manfred Opper

Papers from Tristan Yang's perspective

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

Their papers

• Deep double descent: where bigger models and more data hurt (2019) — 1,162 citations
• SGD on Neural Networks Learns Functions of Increasing Complexity (2019) — 301 citations