Xiulian Peng

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 233 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #17,950 of 21,575 by trust here, #10,010 by citations
• Signal Processing — 72 citations into it; #6,927 of 7,572 by trust here, #5,829 by citations
• Artificial Intelligence — 65 citations into it; #31,956 of 35,345 by trust here, #28,481 by citations
• Speech & Audio — 41 citations into it
• Efficiency & Compression — 23 citations into it
• Computational Mechanics — 19 citations into it
• Media Technology — 13 citations into it
• Model Architectures — 9 citations into it

Whom Xiulian Peng trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Karen Simonyan
7. Yann LeCun
8. Aäron van den Oord
9. Ross B. Girshick
10. Jean-Marc Valin

Who trusts Xiulian Peng overall

1. Wenqi Ren
2. Xue Jiang
3. Zewei He
4. Yanyun Qu
5. Jin-shan Pan
6. Haici Yang
7. Xiaohong Liu
8. Deniz Engin
9. Boyun Li
10. Boyi Li

Papers from Xiulian Peng's perspective

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

Their papers

• AOD-Net: All-in-One Dehazing Network (2017) — 2,090 citations
• Disentangled Feature Learning for Real-Time Neural Speech Coding (2022) — 22 citations
• End-to-End Neural Speech Coding for Real-Time Communications (2022) — 39 citations
• Cross-Scale Vector Quantization for Scalable Neural Speech Coding (2022) — 12 citations
• Interactive Speech and Noise Modeling for Speech Enhancement (2020) — 114 citations
• Frequency-Domain Dynamic Pruning for Convolutional Neural Networks (2018) — 165 citations