Kun Han

Signal Processing · 2013–2015

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 48 citations into it; #2,258 of 7,572 by trust here, #2,343 by citations
• Artificial Intelligence — 33 citations into it; #18,017 of 35,345 by trust here, #17,776 by citations
• Computational Mechanics — 14 citations into it; #3,154 of 10,001 by trust here, #3,126 by citations
• Cognitive Neuroscience — 11 citations into it; #8,526 of 10,852 by trust here, #6,723 by citations
• Experimental and Cognitive Psychology — 6 citations into it; #3,678 of 9,731 by trust here, #4,316 by citations

Whom Kun Han trusts overall

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

1. Deliang Wang
2. Dong Yu
3. Geoffrey E. Hinton
4. Brian Kingsbury
5. Yoshua Bengio
6. John C. Duchi
7. Björn Schuller
8. Li Deng
9. Oriol Vinyals
10. Daniel Povey

Who trusts Kun Han overall

1. Deliang Wang
2. Yuxuan Wang
3. Zhong-Qiu Wang
4. Jinkyu Lee
5. Seyedmahdad Mirsamadi
6. Arun Narayanan
7. Haytham M. Fayek
8. Yi Luo
9. Cassia Valentini-Botinhao
10. Mingyi Chen

Papers from Kun Han's perspective

Ordered by how strongly the researchers Kun Han 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Speech emotion recognition using deep neural network and extreme learning machine (2014) — 745 citations
• Deep neural network based spectral feature mapping for robust speech recognition (2015) — 66 citations
• Learning Spectral Mapping for Speech Dereverberation and Denoising (2015) — 243 citations
• Exploring Monaural Features for Classification-Based Speech Segregation (2013) — 219 citations