Kyuyeon Hwang

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 6 papers; trusts 148 researchers and is trusted by 78 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #2,746 of 35,345 by trust here, #5,600 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #4,176 of 21,575 by trust here, #5,507 by citations
• Signal Processing — 18 citations into it; #1,376 of 7,572 by trust here, #2,343 by citations
• Efficiency & Compression — 16 citations into it; #181 of 1,264 by trust here, #478 by citations
• Model Architectures — 15 citations into it; #494 of 2,278 by trust here, #860 by citations
• Natural Language Processing — 14 citations into it
• Optimization & Training — 13 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Kyuyeon Hwang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. George E. Dahl
4. Ilya Sutskever
5. Yoshua Bengio
6. Alex Graves
7. Yann LeCun
8. Abdel-rahman Mohamed
9. L. Eon Bottou
10. Kai-Fu Lee

Who trusts Kyuyeon Hwang overall

1. Wonyong Sung
2. Sungho Shin
3. Mohammad Rastegari
4. Sajid Anwar
5. Patrick Judd
6. Song Han
7. Matthieu Courbariaux
8. Hessam Bagherinezhad
9. Yoonho Boo
10. Minje Kim

Papers from Kyuyeon Hwang's perspective

Ordered by how strongly the researchers Kyuyeon Hwang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Structured Pruning of Deep Convolutional Neural Networks (2015) — 824 citations
• Fixed point optimization of deep convolutional neural networks for object recognition (2015) — 216 citations
• Fixed-point feedforward deep neural network design using weights +1, 0, and −1 (2014) — 282 citations
• X1000 real-time phoneme recognition VLSI using feed-forward deep neural networks (2014) — 55 citations
• Generative Knowledge Transfer for Neural Language Models (2016) — 12 citations
• Resiliency of Deep Neural Networks under Quantization (2015) — 171 citations