Zheng-Hua Tan

Signal Processing · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 239 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 122 citations into it; #308 of 7,572 by trust here, #519 by citations
• Artificial Intelligence — 83 citations into it; #3,602 of 35,345 by trust here, #6,330 by citations
• Speech & Audio — 54 citations into it; #136 of 1,076 by trust here, #268 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #8,916 of 21,575 by trust here, #12,992 by citations
• Computational Mechanics — 31 citations into it
• Cognitive Neuroscience — 25 citations into it
• Model Architectures — 24 citations into it
• Natural Language Processing — 24 citations into it

Whom Zheng-Hua Tan trusts overall

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

1. P. Loizou
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Alex Krizhevsky
7. Ilya Sutskever
8. Alex Graves
9. Andrew Zisserman
10. Ian J. Goodfellow

Who trusts Zheng-Hua Tan overall

1. Yi Luo
2. Zhuo Chen
3. Sanyuan Chen
4. Zhong-Qiu Wang
5. Jonathan Le Roux
6. Shota Horiguchi
7. Yusuke Fujita
8. Kateřina Žmolíková
9. Alexei Baevski
10. Dong Yu

Papers from Zheng-Hua Tan's perspective

Ordered by how strongly the researchers Zheng-Hua Tan 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification (2017) — 224 citations
• Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks (2017) — 782 citations
• Permutation invariant training of deep models for speaker-independent multi-talker speech separation (2016) — 961 citations
• rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method (2019) — 156 citations
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020) — 337 citations