Linhao Dong

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 110 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #4,621 of 35,345 by trust here, #6,911 by citations
• Natural Language Processing — 49 citations into it; #1,374 of 5,688 by trust here, #1,826 by citations
• Signal Processing — 44 citations into it; #895 of 7,572 by trust here, #880 by citations
• Model Architectures — 33 citations into it
• Speech & Audio — 25 citations into it; #761 of 1,076 by trust here, #783 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Optimization & Training — 8 citations into it

Whom Linhao Dong trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Yoshua Bengio
6. Sepp Hochreiter
7. William Chan
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Navdeep Jaitly

Who trusts Linhao Dong overall

1. Jinyu Li
2. Sanyuan Chen
3. Yu Wu
4. Qian Zhang
5. Julian Salazar
6. Mattia Antonino Di Gangi
7. Kyu J. Han
8. Shiyu Zhou
9. Ahmed M. Ali
10. Jing Zhang

Papers from Linhao Dong's perspective

Ordered by how strongly the researchers Linhao Dong 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition (2018) — 1,188 citations
• Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese (2018) — 130 citations
• A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese (2018) — 69 citations
• Self-attention Aligner: A Latency-control End-to-end Model for ASR Using Self-attention Network and Chunk-hopping (2019) — 95 citations
• Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin (2018) — 23 citations