Zhen Huang

Signal Processing · with Lee · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 71 citations into it; #3,153 of 7,572 by trust here, #2,276 by citations
• Artificial Intelligence — 69 citations into it; #15,907 of 35,345 by trust here, #14,718 by citations
• Speech & Audio — 30 citations into it
• Natural Language Processing — 19 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Mechanics — 8 citations into it

Whom Zhen Huang trusts overall

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

1. Dong Yu
2. Geoffrey E. Hinton
3. Jinyu Li
4. Frank Torsten Bernd Seide
5. George E. Dahl
6. Sabato Marco Siniscalchi
7. Li Deng
8. Radford M. Neal
9. Leo Breiman
10. Chris Leggetter

Who trusts Zhen Huang overall

1. Jinyu Li
2. Zhong Meng
3. Sang-Woo Lee
4. Sabato Marco Siniscalchi
5. Dong Yu
6. Lahiru Samarakoon
7. Yajie Miao
8. Zhuo Chen
9. Chin-Hui Lee
10. Pegah Ghahremani

Papers from Zhen Huang's perspective

Ordered by how strongly the researchers Zhen Huang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Rapid adaptation for deep neural networks through multi-task learning (2015) — 69 citations
• Maximum a posteriori adaptation of network parameters in deep models (2015) — 54 citations
• Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement (2017) — 111 citations
• Feature space maximum a posteriori linear regression for adaptation of deep neural networks (2014) — 21 citations
• Hierarchical Bayesian combination of plug-in maximum a posteriori decoders in deep neural networks-based speech recognition and speaker adaptation (2017) — 13 citations