Yinhao Zhu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 239 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #4,545 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Statistical and Nonlinear Physics — 36 citations into it; #4,752 of 10,127 by trust here, #2,114 by citations
• Statistics and Probability — 31 citations into it; #1,292 of 12,932 by trust here, #1,382 by citations
• Probabilistic & Bayesian Methods — 27 citations into it
• Generative Models — 24 citations into it
• Model Architectures — 23 citations into it
• Computational Mechanics — 20 citations into it

Whom Yinhao Zhu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Qiang Liu
5. Kaiming He
6. Alex Krizhevsky
7. Yann LeCun
8. Radford M. Neal
9. David John Cameron MacKay
10. Sergey Ioffe

Who trusts Yinhao Zhu overall

1. Nicholas Geneva
2. Nicholas Zabaras
3. Rohit Tripathy
4. Shuhao Cao
5. Constantin Grigo
6. Sharmila Karumuri
7. Luning Sun
8. Zong-Yi Li
9. Han Gao
10. Georgios Kissas

Papers from Yinhao Zhu's perspective

Ordered by how strongly the researchers Yinhao Zhu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. 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

• Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification (2018) — 752 citations
• Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data (2019) — 1,046 citations
• Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media (2018) — 342 citations