Peidong Wang

Artificial Intelligence · with Yu · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 85 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #26,597 of 35,345 by trust here, #27,551 by citations
• Natural Language Processing — 46 citations into it; #4,797 of 5,688 by trust here, #5,593 by citations
• Speech & Audio — 44 citations into it; #809 of 1,076 by trust here, #941 by citations
• Signal Processing — 36 citations into it; #7,349 of 7,572 by trust here, #6,083 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 5 citations into it; #2,547 of 2,601 by trust here, #2,428 by citations

Whom Peidong Wang trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Tara N. Sainath
5. Jan Chorowski
6. Diederik P. Kingma
7. Dzmitry Bahdanau
8. Ilya Sutskever
9. Alex Krizhevsky
10. Navdeep Jaitly

Who trusts Peidong Wang overall

1. Jinyu Li
2. Alec Radford
3. Jong Wook Kim

Papers from Peidong Wang's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multitask Training with Text Data for End-to-End Speech Recognition (2020) — 32 citations
• Improving Attention-Based End-to-End ASR Systems with Sequence-Based Loss Functions (2018) — 10 citations
• Token-wise Training for Attention Based End-to-end Speech Recognition (2019) — 5 citations
• Large Margin Training for Attention Based End-to-End Speech Recognition (2019) — 8 citations