Yajie Miao

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 7 papers; trusts 193 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #2,662 of 35,345 by trust here, #5,749 by citations
• Signal Processing — 81 citations into it; #332 of 7,572 by trust here, #575 by citations
• Speech & Audio — 49 citations into it; #172 of 1,076 by trust here, #350 by citations
• Natural Language Processing — 41 citations into it
• Model Architectures — 25 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Yajie Miao trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Dong Yu
4. Frank Torsten Bernd Seide
5. Yoshua Bengio
6. Abdel-rahman Mohamed
7. Tara N. Sainath
8. Hasim Sak
9. Andrew W. Senior
10. Alex Krizhevsky

Who trusts Yajie Miao overall

1. Takaaki Hori
2. Li Deng
3. Jinyu Li
4. Florian Metze
5. Dong Yu
6. Yonatan Belinkov
7. Shinji Watanabe
8. Shaoshi Ling
9. Sercan Ö. Arik
10. Kartik Audhkhasi

Papers from Yajie Miao's perspective

Ordered by how strongly the researchers Yajie Miao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding (2015) — 788 citations
• Deep maxout networks for low-resource speech recognition (2013) — 112 citations
• Speaker Adaptive Training of Deep Neural Network Acoustic Models Using I-Vectors (2015) — 124 citations
• Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training (2013) — 72 citations
• On speaker adaptation of long short-term memory recurrent neural networks (2015) — 44 citations
• Viral Video Style: A Closer Look at Viral Videos on YouTube (2014) — 79 citations
• Extracting deep bottleneck features using stacked auto-encoders (2013) — 307 citations