Dingli Yu

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Scaling & Learning Theory — 33 citations into it
• Optimization & Training — 32 citations into it
• Statistical Learning Theory — 17 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Dingli Yu trusts overall

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

1. Alex Krizhevsky
2. Simon Shaolei Du
3. Geoffrey E. Hinton
4. Kaiming He
5. Sanjeev Arora
6. Radford M. Neal
7. Yoshua Bengio
8. Ilya Sutskever
9. Yann LeCun
10. Christopher K. I. Williams

Papers from Dingli Yu's perspective

Ordered by how strongly the researchers Dingli Yu 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. Learning Multiple Layers of Features from Tiny Images
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
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

• Enhanced Convolutional Neural Tangent Kernels (2019) — 138 citations
• Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks (2019) — 171 citations
• Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee (2019) — 143 citations