Hanrui Wang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 356 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 163 citations into it; #10,898 of 35,345 by trust here, #5,187 by citations
• Computer Vision and Pattern Recognition — 159 citations into it; #7,085 of 21,575 by trust here, #3,645 by citations
• Efficiency & Compression — 67 citations into it; #419 of 1,264 by trust here, #280 by citations
• Model Architectures — 36 citations into it
• Computational Mechanics — 27 citations into it
• Natural Language Processing — 27 citations into it
• Electrical and Electronic Engineering — 20 citations into it
• Geology — 16 citations into it

Whom Hanrui Wang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Quoc V. Le
8. Barret Zoph
9. Yann LeCun
10. Song Han

Who trusts Hanrui Wang overall

1. Song Han
2. Zhanghao Wu
3. Ji Lin
4. Md Maruf Hossain Shuvo
5. Sehoon Kim
6. Ali Hadi Zadeh
7. Zhijian Liu
8. Haotian Tang
9. Swetha Mandava
10. Yi Tay

Papers from Hanrui Wang's perspective

Ordered by how strongly the researchers Hanrui Wang 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• AMC: AutoML for Model Compression and Acceleration on Mobile Devices (2018) — 1,502 citations
• HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020) — 292 citations
• Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution (2020) — 863 citations
• SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning (2020) — 563 citations
• GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning (2020) — 351 citations
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021) — 295 citations