Ryuichi Yamamoto

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 9 papers; trusts 176 researchers and is trusted by 99 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it; #2,490 of 35,345 by trust here, #3,797 by citations
• Signal Processing — 133 citations into it; #273 of 7,572 by trust here, #443 by citations
• Speech & Audio — 103 citations into it; #86 of 1,076 by trust here, #173 by citations
• Natural Language Processing — 66 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Generative Models — 33 citations into it; #1,324 of 1,956 by trust here, #1,502 by citations
• Model Architectures — 31 citations into it
• Optimization & Training — 16 citations into it

Whom Ryuichi Yamamoto trusts overall

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

1. Diederik P. Kingma
2. Eunwoo Song
3. Aäron van den Oord
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Tim Salimans
7. Jimmy Ba
8. Alex Graves
9. Alex Krizhevsky
10. Karen Simonyan

Who trusts Ryuichi Yamamoto overall

1. Xu Tan
2. Yi Ren
3. Wei Ping
4. Jinglin Liu
5. D. Lim
6. Tao Qin
7. Eunwoo Song
8. Rongjie Huang
9. Sang-gil Lee
10. Po-Chun Hsu

Papers from Ryuichi Yamamoto's perspective

Ordered by how strongly the researchers Ryuichi Yamamoto 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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

• A Comparative Study on Transformer vs RNN in Speech Applications (2019) — 796 citations
• Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram (2019) — 1,014 citations
• Probability density distillation with generative adversarial networks for high-quality parallel waveform generation (2019) — 58 citations
• Espnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit (2019) — 233 citations
• Improved Parallel Wavegan Vocoder with Perceptually Weighted Spectrogram Loss (2021) — 18 citations
• Improving LPCNET-Based Text-to-Speech with Linear Prediction-Structured Mixture Density Network (2020) — 14 citations
• TTS-by-TTS: TTS-Driven Data Augmentation for Fast and High-Quality Speech Synthesis (2020) — 36 citations
• Neural Text-to-Speech with a Modeling-by-Generation Excitation Vocoder (2020) — 6 citations
• Semi-Supervised Speaker Adaptation for End-to-End Speech Synthesis with Pretrained Models (2020) — 14 citations