Yuge Shi

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 160 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Generative Models — 16 citations into it
• Optimization & Training — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Multimodal Learning — 8 citations into it
• Model Architectures — 5 citations into it

Whom Yuge Shi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Jimmy Ba
9. Michael I. Jordan
10. Radford M. Neal

Who trusts Yuge Shi overall

1. Kaiyang Zhou
2. Zhenyi Wang

Papers from Yuge Shi's perspective

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

Their papers

• Gradient Matching for Domain Generalization (2021) — 381 citations
• Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models (2019) — 367 citations