Yusuke Yasuda

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 91 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #14,904 of 35,345 by trust here, #11,192 by citations
• Signal Processing — 56 citations into it; #4,770 of 7,572 by trust here, #2,114 by citations
• Speech & Audio — 48 citations into it; #1,002 of 1,076 by trust here, #590 by citations
• Natural Language Processing — 27 citations into it
• Model Architectures — 23 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Generative Models — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Yusuke Yasuda 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. Alex Krizhevsky
6. Ilya Sutskever
7. Jimmy Ba
8. Aäron van den Oord
9. Yann LeCun
10. Ashish Vaswani

Who trusts Yusuke Yasuda overall

1. Xin Eric Wang
2. Erica Cooper
3. Xu Tan
4. Junichi Yamagishi
5. Rui Liu
6. Takuma Okamoto
7. Tomoki Hayashi
8. Momina Masood
9. Tomoki Toda
10. Zhaoxi Mu

Papers from Yusuke Yasuda's perspective

Ordered by how strongly the researchers Yusuke Yasuda 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Zero-Shot Multi-Speaker Text-To-Speech with State-Of-The-Art Neural Speaker Embeddings (2019) — 204 citations
• Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS? (2020) — 25 citations
• Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis (2020) — 3 citations
• Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignments (2019) — 8 citations
• Investigation of Enhanced Tacotron Text-to-speech Synthesis Systems with Self-attention for Pitch Accent Language (2018) — 89 citations