Yu-hsin Chen

Computer Vision and Pattern Recognition · Nature · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 550 researchers and is trusted by 102 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 177 citations into it; #3,074 of 21,575 by trust here, #1,762 by citations
• Artificial Intelligence — 138 citations into it
• Efficiency & Compression — 74 citations into it; #80 of 1,264 by trust here, #173 by citations
• Electrical and Electronic Engineering — 70 citations into it; #189 of 16,539 by trust here, #1,254 by citations
• Model Architectures — 47 citations into it
• Hardware and Architecture — 36 citations into it
• ML Systems & Hardware — 24 citations into it; #154 of 575 by trust here, #145 by citations
• Computer Networks and Communications — 14 citations into it

Whom Yu-hsin Chen trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Kaiming He
6. Yoshua Bengio
7. Ilya Sutskever
8. Andrew Zisserman
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Yu-hsin Chen overall

1. Tien-Ju Yang
2. Vivienne Sze
3. Haichuan Yang
4. Angshuman Parashar
5. Md Maruf Hossain Shuvo
6. Yue Wang
7. Hyoukjun Kwon
8. Joel S. Emer
9. Yingyan Lin
10. Patrick Judd

Papers from Yu-hsin Chen's perspective

Ordered by how strongly the researchers Yu-hsin Chen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning (2016) — 797 citations
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey (2017) — 3,699 citations
• Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks (2016) — 1,634 citations
• Locally-connected and convolutional neural networks for small footprint speaker recognition (2015) — 102 citations
• Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks (2016) — 2,806 citations
• Timeloop: A Systematic Approach to DNN Accelerator Evaluation (2019) — 671 citations
• Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices (2018) — 1,023 citations
• Single-chip microprocessor that communicates directly using light (2015) — 1,218 citations