Kenji Nagamatsu

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 58 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #13,298 of 35,345 by trust here, #15,236 by citations
• Signal Processing — 77 citations into it; #2,522 of 7,572 by trust here, #2,400 by citations
• Speech & Audio — 65 citations into it; #707 of 1,076 by trust here, #900 by citations
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Natural Language Processing — 14 citations into it; #3,442 of 5,688 by trust here, #4,449 by citations
• Optimization & Training — 7 citations into it

Whom Kenji Nagamatsu trusts overall

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

1. Daniel Povey
2. Dong Yu
3. Tom Ko
4. Geoffrey E. Hinton
5. Vijayaditya Peddinti
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ilya Sutskever
9. Sepp Hochreiter
10. Jimmy Ba

Who trusts Kenji Nagamatsu overall

1. Shota Horiguchi
2. Yusuke Fujita
3. Naoyuki Kanda
4. Shinji Watanabe
5. Tae Jin Park

Papers from Kenji Nagamatsu's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. ImageNet classification with deep convolutional neural networks
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Layer Normalization

Their papers

• End-to-End Neural Speaker Diarization with Permutation-Free Objectives (2019) — 312 citations
• Neural Speaker Diarization with Speaker-Wise Chain Rule (2020) — 51 citations
• End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors (2020) — 230 citations
• Acoustic Modeling for Distant Multi-talker Speech Recognition with Single- and Multi-channel Branches (2019) — 45 citations
• Lattice-free State-level Minimum Bayes Risk Training of Acoustic Models (2018) — 29 citations