Minsoo Rhu

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 164 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 74 citations into it; #19,442 of 21,575 by trust here, #5,082 by citations
• Artificial Intelligence — 55 citations into it
• Efficiency & Compression — 45 citations into it
• Electrical and Electronic Engineering — 29 citations into it; #13,537 of 16,539 by trust here, #3,822 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 15 citations into it
• Signal Processing — 6 citations into it

Whom Minsoo Rhu trusts overall

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

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

Who trusts Minsoo Rhu overall

1. Animesh Jain
2. Linghao Song
3. Xuan Peng
4. Amar Phanishayee
5. Udit Gupta
6. Fanjie Kong
7. Wencong Xiao
8. Hai Jin
9. Minjie Wang
10. Chien-chin Huang

Papers from Minsoo Rhu's perspective

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

Their papers

• SCNN: An accelerator for compressed-sparse convolutional neural networks (2017) — 1,254 citations
• vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design (2016) — 477 citations
• Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks (2017) — 193 citations
• Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design (2016) — 32 citations