Sungho Shin

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 278 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it; #9,625 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #9,562 of 21,575 by trust here, #12,172 by citations
• Efficiency & Compression — 68 citations into it
• Model Architectures — 37 citations into it
• Signal Processing — 37 citations into it
• Natural Language Processing — 33 citations into it
• Speech & Audio — 15 citations into it
• Optimization & Training — 14 citations into it

Whom Sungho Shin trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Yoshua Bengio
4. Ilya Sutskever
5. Kaiming He
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Oriol Vinyals
9. Yann LeCun
10. Sergey Zagoruyko

Who trusts Sungho Shin overall

1. Matthew Riemer
2. Miaoyun Zhao
3. Atsuhiro Noguchi
4. Jianping Gou
5. Renzo Andri
6. Asit K. Mishra
7. Sajid Anwar
8. Tim Klinger
9. Yulai Cong
10. Michele M. Franceschini

Papers from Sungho Shin's perspective

Ordered by how strongly the researchers Sungho Shin 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. 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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generative Knowledge Transfer for Neural Language Models (2016) — 12 citations
• Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks (2020) — 37 citations
• Empirical Analysis of Knowledge Distillation Technique for Optimization of Quantized Deep Neural Networks (2019) — 6 citations
• Knowledge Distillation for Optimization of Quantized Deep Neural Networks (2019) — 20 citations
• Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices (2018) — 50 citations
• Resiliency of Deep Neural Networks under Quantization (2015) — 171 citations