Yujun Teng
Statistical and Nonlinear Physics · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 25 citations into it; #2,474 of 10,127 by trust here, #2,708 by citations
- Artificial Intelligence — 24 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it
- AI for Physical Sciences — 15 citations into it; #82 of 247 by trust here, #184 by citations
- Computational Mechanics — 12 citations into it; #8,335 of 10,001 by trust here, #7,173 by citations
- Optimization & Training — 11 citations into it; #1,374 of 2,601 by trust here, #1,571 by citations
- Statistics and Probability — 9 citations into it
- Numerical Methods & Scientific Computing — 6 citations into it
Whom Yujun Teng trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- Yoshua Bengio
- Paris Perdikaris
- Xavier Glorot
- Yann LeCun
- Yibo Yang
- Geoffrey E. Hinton
- Alex Krizhevsky
- Charles-Henri Bruneau
- Diederik P. Kingma
Who trusts Yujun Teng overall
- Shuhao Cao
- Ameya Dilip Jagtap
- Shengze Cai
- Sifan Wang
- Levi D. McClenny
- G. Em Karniadakis
- Xiaowei Jin
- Zhiping Mao
- Haixu Wu
- Chen-Chun Wu
Papers from Yujun Teng's perspective
Ordered by how strongly the researchers Yujun Teng 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (2021) — 1,561 citations
- Understanding and mitigating gradient pathologies in physics-informed neural networks (2020) — 361 citations