Keiichiro Oura

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 6 papers; trusts 24 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #21,042 of 35,345 by trust here, #10,430 by citations
• Signal Processing — 38 citations into it; #4,341 of 7,572 by trust here, #1,388 by citations
• Speech & Audio — 35 citations into it; #718 of 1,076 by trust here, #470 by citations
• Natural Language Processing — 28 citations into it
• Generative Models — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Keiichiro Oura trusts overall

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

1. Keiichi Tokuda
2. Tomoki Toda
3. Heiga Zen
4. K. Shinoda
5. Hideki Kawahara
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Yoshua Bengio
9. Alex Krizhevsky
10. Ikuyo Masuda-Katsuse

Who trusts Keiichiro Oura overall

1. Jinglin Liu
2. Adam Polyak
3. Chengxi Li
4. Prafulla Dhariwal
5. Xu Tan
6. Yi Ren
7. Lior Wolf
8. Keiichi Tokuda

Papers from Keiichiro Oura's perspective

Ordered by how strongly the researchers Keiichiro Oura trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. WaveNet: A Generative Model for Raw Audio

Their papers

• Redefining the Linguistic Context Feature Set for HMM and DNN TTS Through Position and Parsing (2016) — 11 citations
• Speech Synthesis Based on Hidden Markov Models (2013) — 468 citations
• Thousands of Voices for HMM-Based Speech Synthesis–Analysis and Application of TTS Systems Built on Various ASR Corpora (2010) — 102 citations
• Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis (2020) — 27 citations
• Singing Voice Synthesis Based on Generative Adversarial Networks (2019) — 59 citations
• Recent development of the HMM-based singing voice synthesis system - Sinsy (2010) — 100 citations