Kyu J. Han

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 205 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 163 citations into it; #18,759 of 35,345 by trust here, #11,921 by citations
• Signal Processing — 109 citations into it; #4,810 of 7,572 by trust here, #2,664 by citations
• Speech & Audio — 86 citations into it; #997 of 1,076 by trust here, #797 by citations
• Natural Language Processing — 42 citations into it; #4,308 of 5,688 by trust here, #3,754 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Model Architectures — 37 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Evaluation & Benchmarks — 16 citations into it

Whom Kyu J. Han trusts overall

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

1. Daniel Povey
2. Andreas Stolcke
3. Kaiming He
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Alex Graves
7. Yoshua Bengio
8. Diederik P. Kingma
9. Hank Liao
10. Yann LeCun

Who trusts Kyu J. Han overall

1. Qiantong Xu
2. Tatiana Likhomanenko
3. Awni Y. Hannun
4. Abdel-rahman Mohamed
5. Kazuki Irie
6. Weiran Wang
7. Jason Li
8. Gabriel Synnaeve
9. Ronan Collobert
10. Neil Zeghidour

Papers from Kyu J. Han's perspective

Ordered by how strongly the researchers Kyu J. Han 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• The CAPIO 2017 Conversational Speech Recognition System (2017) — 75 citations
• Performance-Efficiency Trade-Offs in Unsupervised Pre-Training for Speech Recognition (2021) — 137 citations
• A Review of Speaker Diarization: Recent Advances with Deep Learning (2021) — 437 citations
• State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention with Dilated 1D Convolutions (2019) — 79 citations
• Densely Connected Networks for Conversational Speech Recognition (2018) — 6 citations