Guoqi Li

Computer Vision and Pattern Recognition · Frontiers in Neuroscience · 2017–2023

A researcher in the Science corpus: 11 papers; trusts 658 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 253 citations into it; #20,356 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 234 citations into it; #33,228 of 35,345 by trust here, #11,921 by citations
• Electrical and Electronic Engineering — 159 citations into it; #1,488 of 16,539 by trust here, #4,166 by citations
• Model Architectures — 111 citations into it
• Efficiency & Compression — 95 citations into it; #1,179 of 1,264 by trust here, #685 by citations
• Cognitive Neuroscience — 92 citations into it; #2,104 of 10,852 by trust here, #2,652 by citations
• Optimization & Training — 48 citations into it
• Cellular and Molecular Neuroscience — 47 citations into it; #2,756 of 2,998 by trust here, #1,427 by citations

Whom Guoqi Li trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Karen Simonyan
7. Ilya Sutskever
8. Diederik P. Kingma
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Guoqi Li overall

1. Wei Fang
2. Man Yao
3. Shi-Wee Deng
4. Zhaofei Yu
5. Yanqing Chen
6. Alexandre D'efossez
7. Lei Deng
8. By Lei Deng
9. Ying Wang
10. Rudra P. K. Poudel

Papers from Guoqi Li's perspective

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

Their papers

• Training and Inference with Integers in Deep Neural Networks (2018) — 419 citations
• $L1$ -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
• CIFAR10-DVS: An Event-Stream Dataset for Object Classification (2017) — 710 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
• Spike-driven Transformer (2023) — 315 citations
• Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (2020) — 940 citations