Lei Deng

Electrical and Electronic Engineering · AAAI Conference on Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 116 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 105 citations into it; #2,014 of 16,539 by trust here, #4,543 by citations
• Artificial Intelligence — 104 citations into it
• Computer Vision and Pattern Recognition — 96 citations into it
• Cognitive Neuroscience — 75 citations into it; #2,053 of 10,852 by trust here, #2,652 by citations
• Cellular and Molecular Neuroscience — 40 citations into it
• Model Architectures — 38 citations into it
• Optimization & Training — 24 citations into it
• Graph Learning — 12 citations into it

Whom Lei Deng trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Karen Simonyan
9. Sergey Ioffe
10. Jimmy Ba

Who trusts Lei Deng overall

1. Wei Fang
2. Man Yao
3. Shi-Wee Deng
4. Zhaofei Yu
5. Yuhang Li
6. Mingqing Xiao
7. Yanqing Chen
8. Yonghong Tian
9. Nitin Rathi
10. Gopalakrishnan Srinivasan

Papers from Lei Deng's perspective

Ordered by how strongly the researchers Lei Deng 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks (2018) — 128 citations
• Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks (2017) — 1,368 citations
• Towards artificial general intelligence with hybrid Tianjic chip architecture (2019) — 1,085 citations
• Direct Training for Spiking Neural Networks: Faster, Larger, Better (2018) — 797 citations
• Going Deeper With Directly-Trained Larger Spiking Neural Networks (2020) — 736 citations
• Rethinking the performance comparison between SNNS and ANNS (2020) — 355 citations
• Attention Spiking Neural Networks (2022) — 303 citations
• HyGCN: A GCN Accelerator with Hybrid Architecture (2020) — 351 citations