Yi Zhao

Artificial Intelligence · with Yamagishi · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 41 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #29,711 of 35,345 by trust here, #19,439 by citations
• Signal Processing — 56 citations into it; #6,398 of 7,572 by trust here, #3,428 by citations
• Speech & Audio — 47 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Natural Language Processing — 18 citations into it
• Generative Models — 17 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Yi Zhao trusts overall

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

1. Heiga Zen
2. Aäron van den Oord
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Graves
6. Yoshua Bengio
7. Oriol Vinyals
8. Jimmy Ba
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Yi Zhao overall

1. Hieu-Thi Luong
2. Ryuichi Yamamoto
3. Kasperi Palkama
4. Xu Tan
5. Junichi Yamagishi
6. Jennifer Williams
7. Pritish Chandna
8. Xin Eric Wang
9. Eunwoo Song
10. Lauri Juvela

Papers from Yi Zhao's perspective

Ordered by how strongly the researchers Yi Zhao 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. GENERATIVE ADVERSARIAL NETS
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction (2020) — 21 citations
• Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE Paradigm (2020) — 27 citations
• Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis (2020) — 3 citations
• Wasserstein GAN and Waveform Loss-Based Acoustic Model Training for Multi-Speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder (2018) — 67 citations
• Speaker Representations for Speaker Adaptation in Multiple Speakers' BLSTM-RNN-Based Speech Synthesis (2016) — 21 citations