Shaohuai Shi

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 94 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #31,086 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #18,998 of 21,575 by trust here, #7,499 by citations
• Optimization & Training — 37 citations into it
• Efficiency & Compression — 18 citations into it
• Model Architectures — 17 citations into it
• Computer Networks and Communications — 12 citations into it
• Electrical and Electronic Engineering — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Shaohuai Shi trusts overall

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

1. Yann LeCun
2. Alex Graves
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yoshua Bengio
8. J. Urgen Schmidhuber
9. Sepp Hochreiter
10. Diederik P. Kingma

Who trusts Shaohuai Shi overall

1. William Grant Hatcher
2. M. Mohammadi
3. Wei Yu
4. Yu Emma Wang
5. Ala Al-Fuqaha

Papers from Shaohuai Shi's perspective

Ordered by how strongly the researchers Shaohuai Shi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes (2018) — 423 citations
• Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection (2020) — 72 citations
• Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020) — 61 citations
• Benchmarking State-of-the-Art Deep Learning Software Tools (2016) — 343 citations