Xiaohui Zhang

Artificial Intelligence · with Povey · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 86 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #3,870 of 35,345 by trust here, #6,378 by citations
• Signal Processing — 80 citations into it; #532 of 7,572 by trust here, #671 by citations
• Speech & Audio — 69 citations into it; #439 of 1,076 by trust here, #420 by citations
• Natural Language Processing — 51 citations into it
• Model Architectures — 38 citations into it; #1,016 of 2,278 by trust here, #1,111 by citations
• Optimization & Training — 26 citations into it; #636 of 2,601 by trust here, #1,195 by citations
• Computer Vision and Pattern Recognition — 19 citations into it

Whom Xiaohui Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Daniel Povey
4. Alex Graves
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Krizhevsky
8. George E. Dahl
9. Abdel-rahman Mohamed
10. Dong Yu

Who trusts Xiaohui Zhang overall

1. Daniel Povey
2. Vijayaditya Peddinti
3. Vimal Manohar
4. David Snyder
5. Sanjeev Khudanpur
6. László Tóth
7. Daniel Garcia-Romero
8. Kai Chen
9. James Martens
10. Hang Su

Papers from Xiaohui Zhang's perspective

Ordered by how strongly the researchers Xiaohui Zhang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Improving deep neural network acoustic models using generalized maxout networks (2014) — 315 citations
• Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging (2014) — 250 citations
• Transformer-Based Acoustic Modeling for Hybrid Speech Recognition (2019) — 266 citations
• From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition (2019) — 65 citations
• Improving speaker recognition performance in the domain adaptation challenge using deep neural networks (2014) — 97 citations
• Fast, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces (2020) — 20 citations