Yanmin Qian

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 11 papers; trusts 190 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it; #12,460 of 35,345 by trust here, #6,266 by citations
• Signal Processing — 199 citations into it; #2,356 of 7,572 by trust here, #650 by citations
• Speech & Audio — 109 citations into it; #187 of 1,076 by trust here, #50 by citations
• Natural Language Processing — 43 citations into it
• Model Architectures — 39 citations into it; #1,749 of 2,278 by trust here, #1,794 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• Efficiency & Compression — 12 citations into it

Whom Yanmin Qian trusts overall

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

1. Dong Yu
2. Tara N. Sainath
3. Geoffrey E. Hinton
4. Abdel-rahman Mohamed
5. Yann LeCun
6. Yoshua Bengio
7. Karen Simonyan
8. Alex Krizhevsky
9. Daniel Povey
10. Hasim Sak

Who trusts Yanmin Qian overall

1. Yonatan Belinkov
2. Wayne Xiong
3. Andreas Stolcke
4. Jasha Droppo
5. Zhao You
6. Dong Yu
7. Khe Chai Sim
8. Marc Delcroix
9. Mirco Ravanelli
10. Shinji Watanabe

Papers from Yanmin Qian's perspective

Ordered by how strongly the researchers Yanmin Qian 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021) — 3,371 citations
• Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition (2016) — 332 citations
• Knowledge Distillation for Sequence Model (2018) — 70 citations
• Reshaping deep neural network for fast decoding by node-pruning (2014) — 121 citations
• Cluster adaptive training for deep neural network (2015) — 75 citations
• Very deep convolutional neural networks for robust speech recognition (2016) — 83 citations
• What Does the Speaker Embedding Encode? (2017) — 61 citations
• Wespeaker: A Research and Production Oriented Speaker Embedding Learning Toolkit (2022) — 247 citations
• Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training (2017) — 87 citations
• Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition (2017) — 14 citations
• The Kaldi Speech Recognition Toolkit (2011) — 6,607 citations