Tailin Wu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 46 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #16,449 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Graph Learning — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Statistical and Nonlinear Physics — 13 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Information Systems — 5 citations into it

Whom Tailin Wu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Oriol Vinyals
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Naftali Tishby
8. Ilya Sutskever
9. Jimmy Ba
10. Yann LeCun

Who trusts Tailin Wu overall

1. Siqi Miao
2. Susheel Suresh
3. Pan Li
4. Curtis G. Northcutt
5. Jessa Bekker
6. Zeyu Feng
7. Davood Karimi
8. Alexandre Louis Lamy
9. Miaoyuan Liu

Papers from Tailin Wu's perspective

Ordered by how strongly the researchers Tailin Wu 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels (2017) — 174 citations
• Graph Information Bottleneck (2020) — 350 citations
• Meta-learning autoencoders for few-shot prediction (2018) — 24 citations