Hirofumi Inaguma

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 7 papers; trusts 68 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it; #26,082 of 35,345 by trust here, #8,210 by citations
• Natural Language Processing — 80 citations into it
• Speech & Audio — 63 citations into it; #799 of 1,076 by trust here, #361 by citations
• Signal Processing — 60 citations into it
• Model Architectures — 39 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Optimization & Training — 5 citations into it

Whom Hirofumi Inaguma trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Dzmitry Bahdanau
5. Diederik P. Kingma
6. Matthew D. Zeiler
7. Sepp Hochreiter
8. Ilya Sutskever
9. Alex Krizhevsky
10. Oriol Vinyals

Who trusts Hirofumi Inaguma overall

1. Jinyu Li
2. Renjie Zheng
3. Changhan Wang
4. Kurniawati Azizah
5. Alexis Conneau
6. Oliver Adams
7. Takatomo Kano
8. Rong Ye
9. Jennifer Williams
10. Richard He Bai

Papers from Hirofumi Inaguma's perspective

Ordered by how strongly the researchers Hirofumi Inaguma 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Comparative Study on Transformer vs RNN in Speech Applications (2019) — 796 citations
• Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion (2018) — 46 citations
• Recent Developments on Espnet Toolkit Boosted By Conformer (2020) — 292 citations
• Distilling the Knowledge of BERT for Sequence-to-Sequence ASR (2020) — 59 citations
• Seamless: Multilingual Expressive and Streaming Speech Translation (2023) — 299 citations
• SeamlessM4T: Massively Multilingual&Multimodal Machine Translation (2023) — 202 citations
• ESPnet-ST: All-in-One Speech Translation Toolkit (2020) — 174 citations