Lei Zhang

Artificial Intelligence · with Chen · 2019–2022

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #19,606 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Signal Processing — 12 citations into it; #4,038 of 7,572 by trust here, #3,309 by citations
• Management Science and Operations Research — 11 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Lei Zhang trusts overall

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

1. Diederik P. Kingma
2. Guillaume Chevillon
3. S. Brandt
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Dzmitry Bahdanau
8. William S. Dorn
9. Jimmy Ba
10. Kaiming He

Who trusts Lei Zhang overall

1. Donghao Luo
2. Yunhao Zhang

Papers from Lei Zhang's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Continually from Low-shot Data Stream (2019) — 4 citations
• Are Transformers Effective for Time Series Forecasting? (2022) — 4,138 citations