Shuang Xu

Artificial Intelligence · with Xu · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 384 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 204 citations into it; #4,281 of 35,345 by trust here, #5,788 by citations
• Computer Vision and Pattern Recognition — 138 citations into it
• Natural Language Processing — 74 citations into it; #1,361 of 5,688 by trust here, #1,826 by citations
• Signal Processing — 60 citations into it; #762 of 7,572 by trust here, #731 by citations
• Model Architectures — 43 citations into it
• Speech & Audio — 38 citations into it; #1,059 of 1,076 by trust here, #923 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 14 citations into it

Whom Shuang Xu trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Jimmy Ba
10. Sepp Hochreiter

Who trusts Shuang Xu overall

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

Papers from Shuang Xu's perspective

Ordered by how strongly the researchers Shuang Xu 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Going deeper with convolutions

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
• VLP: A Survey on Vision-language Pre-training (2022) — 352 citations
• First Step Towards End-to-End Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention (2016) — 61 citations
• A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese (2018) — 69 citations
• Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020) — 53 citations
• CBLDNN-Based Speaker-Independent Speech Separation Via Generative Adversarial Training (2018) — 41 citations
• Multidimensional Residual Learning Based on Recurrent Neural Networks for Acoustic Modeling (2016) — 23 citations