Gakuto Kurata

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 121 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #20,273 of 35,345 by trust here, #9,641 by citations
• Signal Processing — 55 citations into it
• Speech & Audio — 37 citations into it; #1,001 of 1,076 by trust here, #516 by citations
• Natural Language Processing — 33 citations into it
• Model Architectures — 27 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Optimization & Training — 13 citations into it
• Efficiency & Compression — 9 citations into it

Whom Gakuto Kurata trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jimmy Ba
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yann LeCun
9. Oriol Vinyals
10. Ronald J. Williams

Who trusts Gakuto Kurata overall

1. Thibault Doutre
2. Samuel Louvan
3. Kang Min Yoo
4. Yu Wang
5. Kartik Audhkhasi
6. Yutai Hou
7. Alexandros Papangelis
8. Wei Han
9. Yilin Shen
10. George Saon

Papers from Gakuto Kurata's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• English Conversational Telephone Speech Recognition by Humans and Machines (2017) — 374 citations
• Improved Knowledge Distillation from Bi-Directional to Uni-Directional LSTM CTC for End-to-End Speech Recognition (2018) — 44 citations
• Knowledge Distillation from Offline to Streaming RNN Transducer for End-to-End Speech Recognition (2020) — 55 citations
• Efficient Knowledge Distillation from an Ensemble of Teachers (2017) — 280 citations
• Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling (2016) — 130 citations