Ting Yu

Artificial Intelligence · with Wu · 2005–2018

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #5,432 of 35,345 by trust here, #11,624 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 7 citations into it

Whom Ting Yu trusts overall

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

1. Mark J van Rossum
2. Diederik P. Kingma
3. Billy M. Williams
4. Geoffrey E. Hinton
5. L. Eon Bottou
6. Jimmy Ba
7. Yann LeCun
8. Yoshua Bengio
9. Yexin Li
10. Kaiming He

Who trusts Ting Yu overall

1. Zezhi Shao
2. Zhiyong Cui
3. Lei Bai
4. Shengna Guo
5. Mengzhang Li
6. Ailin Deng
7. Corey Snyder
8. Chao Shang
9. Rajat Sen
10. Youfang Lin

Papers from Ting Yu's perspective

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

• The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects (2018) — 43 citations
• Space Weather Modeling Framework: A new tool for the space science community (2005) — 698 citations
• The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems (2017) — 1,879 citations
• Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting (2017) — 5,169 citations