Lei Huang

Artificial Intelligence · with Carbonell · 2018

A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Optimization & Training — 10 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Model Architectures — 5 citations into it

Whom Lei Huang trusts overall

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

1. John C. Duchi
2. Kaiming He
3. Diederik P. Kingma
4. Kfir Yehuda Levy
5. Sergey Ioffe
6. Yoon Kim
7. Sepp Hochreiter
8. Geoffrey E. Hinton
9. Elad Hazan
10. X. Zhang

Who trusts Lei Huang overall

1. Lei Huang
2. Esteban Real

Papers from Lei Huang's perspective

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

Their papers

• BLOCK-NORMALIZED GRADIENT METHOD: AN EMPIRICAL STUDY FOR TRAINING DEEP NEURAL NETWORK (2018) — 36 citations