Kei Hashimoto

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #27,467 of 35,345 by trust here, #25,040 by citations
• Signal Processing — 24 citations into it; #6,094 of 7,572 by trust here, #5,340 by citations
• Speech & Audio — 20 citations into it; #890 of 1,076 by trust here, #1,043 by citations
• Generative Models — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it

Whom Kei Hashimoto trusts overall

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

1. Keiichi Tokuda
2. Heiga Zen
3. Geoffrey E. Hinton
4. Martin A. Riedmiller
5. Diederik P. Kingma
6. Yoshua Bengio
7. Alex Krizhevsky
8. Satoshi Imai
9. Tomoki Toda
10. Takao Kobayashi

Who trusts Kei Hashimoto overall

1. Keiichi Tokuda
2. Xu Tan
3. Heiga Zen
4. Pritish Chandna

Papers from Kei Hashimoto's perspective

Ordered by how strongly the researchers Kei Hashimoto 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet: A large-scale hierarchical image database

Their papers

• Integration of Spectral Feature Extraction and Modeling for HMM-Based Speech Synthesis (2014) — 5 citations
• Redefining the Linguistic Context Feature Set for HMM and DNN TTS Through Position and Parsing (2016) — 11 citations
• Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis (2020) — 27 citations
• Singing Voice Synthesis Based on Generative Adversarial Networks (2019) — 59 citations