Shi Gu

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Electrical and Electronic Engineering — 18 citations into it
• Optimization & Training — 15 citations into it
• Cognitive Neuroscience — 12 citations into it

Whom Shi Gu trusts overall

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

1. Alex Krizhevsky
2. Yujie Wu
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Kaiming He
6. Lei Deng
7. James Martens

Papers from Shi Gu's perspective

Ordered by how strongly the researchers Shi Gu 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. Learning Multiple Layers of Features from Tiny Images
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Gradient-based learning applied to document recognition
7. Distilling the Knowledge in a Neural Network (arXiv.org)
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction (2021) — 675 citations
• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting (2022) — 383 citations