Yibo Yang

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 10 papers; trusts 820 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #22,533 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 192 citations into it; #5,191 of 21,575 by trust here, #5,082 by citations
• Generative Models — 45 citations into it
• Statistical and Nonlinear Physics — 43 citations into it; #779 of 10,127 by trust here, #1,239 by citations
• Statistics and Probability — 43 citations into it; #2,999 of 12,932 by trust here, #3,330 by citations
• Model Architectures — 38 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it
• Optimization & Training — 36 citations into it

Whom Yibo Yang trusts overall

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

1. Diederik P. Kingma
2. Maizar Raissi
3. Alex Krizhevsky
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Kaiming He
7. Radford M. Neal
8. Ross B. Girshick
9. Yann LeCun
10. Ilya Sutskever

Who trusts Yibo Yang overall

1. Sifan Wang
2. Han Gao
3. Ameya Dilip Jagtap
4. Salvatore Cuomo
5. Stefania Fresca
6. Paris Perdikaris
7. Luning Sun
8. Cynthia Rudin
9. Jared D. Willard
10. Xuhui Meng

Papers from Yibo Yang's perspective

Ordered by how strongly the researchers Yibo Yang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Expectation-Maximization Attention Networks for Semantic Segmentation (2019) — 645 citations
• SOGNet: Scene Overlap Graph Network for Panoptic Segmentation (2019) — 64 citations
• Convolutional Neural Networks with Alternately Updated Clique (2018) — 130 citations
• Adversarial Uncertainty Quantification in Physics-Informed Neural Networks (2018) — 424 citations
• Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (2019) — 63 citations
• Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning (2023) — 177 citations
• Improving Inference for Neural Image Compression (2020) — 144 citations
• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020)
• Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning (2019) — 483 citations
• Physics-informed deep generative models (2018) — 70 citations