Qiang Huo

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #6,695 of 35,345 by trust here, #15,004 by citations
• Signal Processing — 20 citations into it; #2,924 of 7,572 by trust here, #5,570 by citations
• Optimization & Training — 12 citations into it; #910 of 2,601 by trust here, #1,585 by citations
• Speech & Audio — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #6,100 of 21,575 by trust here, #11,950 by citations
• Natural Language Processing — 9 citations into it
• Model Architectures — 6 citations into it

Whom Qiang Huo trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Dong Yu
4. Ilya Sutskever
5. Navdeep Jaitly
6. Li Deng
7. Yoshua Bengio
8. George E. Dahl
9. Abdel-rahman Mohamed
10. Brian Kingsbury

Who trusts Qiang Huo overall

1. Li Deng
2. Jianyu Wang
3. Tal Ben-Nun
4. Sree Hari Krishnan Parthasarathi
5. Tao Lin
6. Dong Yu
7. Zhen Huang
8. Andros Tjandra
9. Chao Weng
10. Mengxiao Bi

Papers from Qiang Huo's perspective

Ordered by how strongly the researchers Qiang Huo 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering (2016) — 146 citations
• A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR (2013) — 56 citations