Masato Mimura

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 4 papers; trusts 66 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #25,865 of 35,345 by trust here, #24,417 by citations
• Signal Processing — 43 citations into it; #6,439 of 7,572 by trust here, #5,086 by citations
• Natural Language Processing — 39 citations into it
• Speech & Audio — 38 citations into it; #789 of 1,076 by trust here, #869 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 Masato Mimura trusts overall

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

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

Who trusts Masato Mimura overall

1. Mirco Ravanelli
2. Maurizio Omologo

Papers from Masato Mimura's perspective

Ordered by how strongly the researchers Masato Mimura 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (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