Suyoun Kim

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 93 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #5,049 of 35,345 by trust here, #7,010 by citations
• Signal Processing — 40 citations into it; #719 of 7,572 by trust here, #761 by citations
• Natural Language Processing — 35 citations into it
• Speech & Audio — 25 citations into it; #226 of 1,076 by trust here, #295 by citations
• Model Architectures — 20 citations into it; #1,591 of 2,278 by trust here, #1,475 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 6 citations into it; #2,350 of 2,601 by trust here, #2,378 by citations

Whom Suyoun Kim trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Sepp Hochreiter
5. Dzmitry Bahdanau
6. Ilya Sutskever
7. J. Urgen Schmidhuber
8. Jan Chorowski
9. Abdel-rahman Mohamed
10. Matthew D. Zeiler

Who trusts Suyoun Kim overall

1. Takaaki Hori
2. Shinji Watanabe
3. Jinyu Li
4. Andros Tjandra
5. Dong Yu
6. Hirofumi Inaguma
7. Sakriani Watiasri Sakti
8. Gakuto Kurata
9. Ali Asghar Zare
10. John R. Hershey

Papers from Suyoun Kim's perspective

Ordered by how strongly the researchers Suyoun Kim 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Hybrid CTC/Attention Architecture for End-to-End Speech Recognition (2017) — 882 citations
• Joint CTC-attention based end-to-end speech recognition using multi-task learning (2016) — 1,000 citations
• Improved training for online end-to-end speech recognition systems (2017) — 47 citations
• Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition (2014) — 33 citations