Guofei Pang
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 6 papers; trusts 66 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 61 citations into it; #14,678 of 35,345 by trust here, #14,718 by citations
- Modeling and Simulation — 57 citations into it; #403 of 2,105 by trust here, #605 by citations
- Statistical and Nonlinear Physics — 41 citations into it; #1,095 of 10,127 by trust here, #1,838 by citations
- Computational Theory and Mathematics — 40 citations into it; #6,914 of 12,182 by trust here, #6,463 by citations
- Applied Mathematics — 39 citations into it
- Numerical Analysis — 28 citations into it
- Mathematical Physics — 24 citations into it
- Statistics and Probability — 22 citations into it; #12,211 of 12,932 by trust here, #10,855 by citations
Whom Guofei Pang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- Paris Perdikaris
- G. Em Karniadakis
- Geoffrey E. Hinton
- Carl E. Rasmussen
- Hong Wang
- Diederik P. Kingma
- Yoshua Bengio
- Xavier Glorot
- Alex Krizhevsky
Who trusts Guofei Pang overall
- Lu Lu
- G. Em Karniadakis
- Liu Yang
- Xuhui Meng
- Cynthia Rudin
- Pengzhan Jin
- Zhiping Mao
- Maizar Raissi
- Shengze Cai
- Benjamin Moseley
Papers from Guofei Pang's perspective
Ordered by how strongly the researchers Guofei Pang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (2017) — 58 citations
- Neural-net-induced Gaussian process regression for function approximation and PDE solution (2018) — 80 citations
- fPINNs: Fractional Physics-Informed Neural Networks (2018) — 975 citations
- What is the fractional Laplacian? A comparative review with new results (2018) — 476 citations
- Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (2019) — 4,061 citations
- Space-fractional advection-dispersion equations by the Kansa method (2015) — 136 citations