Sungjoo Yoo

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 240 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it; #9,092 of 21,575 by trust here, #3,361 by citations
• Artificial Intelligence — 96 citations into it; #15,310 of 35,345 by trust here, #12,387 by citations
• Hardware and Architecture — 50 citations into it; #1,414 of 1,551 by trust here, #318 by citations
• Efficiency & Compression — 40 citations into it; #509 of 1,264 by trust here, #290 by citations
• Computer Networks and Communications — 33 citations into it; #9,368 of 9,886 by trust here, #2,136 by citations
• Model Architectures — 30 citations into it
• Natural Language Processing — 18 citations into it
• Optimization & Training — 17 citations into it

Whom Sungjoo Yoo trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. X. Zhang
6. Christian Szegedy
7. Sungpack Hong
8. Ilya Sutskever
9. Yann LeCun
10. Shaoqing Ren

Who trusts Sungjoo Yoo overall

1. Yunhui Guo
2. Jungwook Choi
3. Onur Mutlu
4. Shaohui Lin
5. Duseok Kang
6. Frederick Tung
7. Shuangchen Li
8. Tien-Ju Yang
9. Jean Kossaifi

Papers from Sungjoo Yoo's perspective

Ordered by how strongly the researchers Sungjoo Yoo 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. Long Short-Term Memory
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications (2015) — 949 citations
• Weighted-Entropy-Based Quantization for Deep Neural Networks (2017) — 272 citations
• PIM-enabled instructions: A low-overhead, locality-aware processing-in-memory architecture (2015) — 543 citations
• Big/little deep neural network for ultra low power inference (2015) — 151 citations
• Machine Learning at Facebook: Understanding Inference at the Edge (2019) — 490 citations
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations
• A scalable processing-in-memory accelerator for parallel graph processing (2015) — 900 citations