Hao-Jun Michael Shi

Optimization & Training · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 37 citations into it
• Artificial Intelligence — 35 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Computational Mechanics — 12 citations into it

Whom Hao-Jun Michael Shi trusts overall

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

1. Alex Krizhevsky
2. Jorge Nocedal
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Kaiming He
6. Richard H. Byrd
7. Yann LeCun
8. Sergey Ioffe
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Hao-Jun Michael Shi overall

1. Zi-Mei Yao

Papers from Hao-Jun Michael Shi's perspective

Ordered by how strongly the researchers Hao-Jun Michael 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Their papers

• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• A Progressive Batching L-BFGS Method for Machine Learning (2018) — 176 citations