Zhiru Zhang

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 7 papers; trusts 287 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #15,441 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 75 citations into it; #25,139 of 35,345 by trust here, #15,866 by citations
• Efficiency & Compression — 54 citations into it; #921 of 1,264 by trust here, #913 by citations
• Model Architectures — 37 citations into it
• Computer Networks and Communications — 13 citations into it
• Optimization & Training — 13 citations into it
• Signal Processing — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Zhiru Zhang trusts overall

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

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

Who trusts Zhiru Zhang overall

1. Young Geun Kim
2. Yizeng Han

Papers from Zhiru Zhang's perspective

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

Their papers

• Channel Gating Neural Networks (2018) — 207 citations
• Building Efficient Deep Neural Networks With Unitary Group Convolutions (2018) — 29 citations
• Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating (2019) — 48 citations
• Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs (2017) — 416 citations
• Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks (2018) — 298 citations
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting (2019) — 351 citations
• High-Level Synthesis for FPGAs: From Prototyping to Deployment (2011) — 839 citations