Shinsuke Sakai

Artificial Intelligence · 2008–2021

A researcher in the Science corpus: 6 papers; trusts 67 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #25,864 of 35,345 by trust here, #24,417 by citations
• Signal Processing — 44 citations into it; #6,437 of 7,572 by trust here, #5,086 by citations
• Natural Language Processing — 39 citations into it
• Speech & Audio — 39 citations into it; #516 of 1,076 by trust here, #570 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Shinsuke Sakai trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Diederik P. Kingma
5. Dong Yu
6. Alex Krizhevsky
7. Li Deng
8. Yann LeCun
9. Ilya Sutskever
10. Nelson Morgan

Who trusts Shinsuke Sakai overall

1. Mirco Ravanelli
2. Takashi Morita
3. Andy T. Liu
4. Patrick Lumban Tobing

Papers from Shinsuke Sakai's perspective

Ordered by how strongly the researchers Shinsuke Sakai 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. 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

• Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature (2015) — 34 citations
• Semi-supervised ensemble DNN acoustic model training (2017) — 19 citations
• Distilling the Knowledge of BERT for Sequence-to-Sequence ASR (2020) — 59 citations
• Data Augmentation for ASR Using TTS Via a Discrete Representation (2021) — 26 citations
• Development of Indonesian Large Vocabulary Continuous Speech Recognition System within A-STAR Project (2008) — 63 citations
• Development of HMM-based Indonesian Speech Synthesis (2008)