Ling Guo

Artificial Intelligence · with Karniadakis · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 60 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #7,091 of 35,345 by trust here, #13,353 by citations
• Statistical and Nonlinear Physics — 30 citations into it; #1,486 of 10,127 by trust here, #2,503 by citations
• Statistics and Probability — 28 citations into it; #2,643 of 12,932 by trust here, #2,919 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Computational Mechanics — 15 citations into it
• AI for Physical Sciences — 10 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Numerical Methods & Scientific Computing — 10 citations into it

Whom Ling Guo trusts overall

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

1. Dongbin Xiu
2. Maizar Raissi
3. Youssef M. Marzouk
4. G. Em Karniadakis
5. Andrew M. Stuart
6. Isaac E. Lagaris
7. Diederik P. Kingma
8. Radford M. Neal
9. Yarin Gal
10. Geoffrey E. Hinton

Who trusts Ling Guo overall

1. Lu Lu
2. Guofei Pang
3. G. Em Karniadakis
4. Xuhui Meng
5. Zixue Xiang
6. Salvatore Cuomo
7. Liu Yang
8. Luning Sun
9. Han Gao
10. Chengping Rao

Papers from Ling Guo's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. GENERATIVE ADVERSARIAL NETS
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (2018) — 528 citations
• Stochastic Collocation Algorithms Using l1-Minimization for Bayesian Solution of Inverse Problems (2015) — 32 citations
• Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks (2019) — 294 citations