Huishuai Zhang

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it
• Natural Language Processing — 32 citations into it
• Optimization & Training — 31 citations into it; #2,259 of 2,601 by trust here, #2,519 by citations
• Model Architectures — 26 citations into it; #2,240 of 2,278 by trust here, #874 by citations
• Scaling & Learning Theory — 14 citations into it; #350 of 428 by trust here, #417 by citations
• Computational Mechanics — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Huishuai Zhang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Zeyuan Allen-Zhu
4. Yann LeCun
5. Yuanzhi Li
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Diederik P. Kingma
9. L. Eon Bottou
10. X. Zhang

Who trusts Huishuai Zhang overall

1. Difan Zou
2. Quanquan Gu

Papers from Huishuai Zhang's perspective

Ordered by how strongly the researchers Huishuai Zhang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• Differentially Private Fine-tuning of Language Models (2021) — 517 citations
• On Layer Normalization in the Transformer Architecture (2020) — 1,522 citations
• Training Over-parameterized Deep ResNet Is almost as Easy as Training a Two-layer Network (2019) — 15 citations