Tao Tu

Speech & Audio · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Speech & Audio — 47 citations into it
• Artificial Intelligence — 46 citations into it; #15,316 of 35,345 by trust here, #17,776 by citations
• Signal Processing — 30 citations into it; #4,768 of 7,572 by trust here, #5,086 by citations
• Natural Language Processing — 19 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Tao Tu trusts overall

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

1. Daniel W. Griffin
2. Alex Graves
3. Yoshua Bengio
4. Yuxuan Wang
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Slava Shechtman
8. Oriol Vinyals
9. Aäron van den Oord
10. Alex Krizhevsky

Who trusts Tao Tu overall

1. Xu Tan
2. Mutian He
3. Tao Qin
4. Kurniawati Azizah
5. Frank K. Soong
6. Jingzhou Yang
7. Katsuki Inoue
8. Tie-Yan Liu
9. Alexei Baevski
10. Yu-An Chung

Papers from Tao Tu's perspective

Ordered by how strongly the researchers Tao Tu 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning (2019) — 53 citations
• Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation (2020) — 8 citations
• End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning (2019) — 100 citations