Tatsuya Kawahara

Artificial Intelligence · 2001–2021

A researcher in the Science corpus: 9 papers; trusts 111 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #17,176 of 35,345 by trust here, #17,110 by citations
• Natural Language Processing — 73 citations into it
• Signal Processing — 56 citations into it; #6,438 of 7,572 by trust here, #5,086 by citations
• Speech & Audio — 53 citations into it; #757 of 1,076 by trust here, #738 by citations
• Model Architectures — 18 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Tatsuya Kawahara trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Andreas Stolcke
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Thorsten Joachims

Who trusts Tatsuya Kawahara overall

1. Mirco Ravanelli
2. Seonghan Ryu
3. Graham W. Taylor
4. Joo-Kyung Kim
5. Sheng Li
6. Gökhan Tür
7. Ian Lane
8. Sandrine Brognaux
9. Detai Xin
10. Jinyu Li

Papers from Tatsuya Kawahara's perspective

Ordered by how strongly the researchers Tatsuya Kawahara 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
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. ImageNet: A large-scale hierarchical image database

Their papers

• Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion (2018) — 46 citations
• 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
• Machine Translation without Words through Substring Alignment (2012) — 44 citations
• Recent Development of Open-Source Speech Recognition Engine Julius (2009) — 348 citations
• Out-of-Domain Utterance Detection Using Classification Confidences of Multiple Topics (2007) — 63 citations
• Julius - an open source real-time large vocabulary recognition engine (2001) — 660 citations