Yingyan Lin

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 9 papers; trusts 594 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 224 citations into it; #19,933 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 198 citations into it; #32,437 of 35,345 by trust here, #15,004 by citations
• Efficiency & Compression — 123 citations into it
• Model Architectures — 55 citations into it
• Reinforcement Learning — 53 citations into it
• Optimization & Training — 40 citations into it
• Electrical and Electronic Engineering — 33 citations into it; #11,679 of 16,539 by trust here, #12,727 by citations
• Computational Theory and Mathematics — 9 citations into it

Whom Yingyan Lin trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Karen Simonyan
9. Richard S. Sutton
10. Ross B. Girshick

Who trusts Yingyan Lin overall

1. Young Geun Kim
2. Junru Wu
3. Yonggan Fu
4. Yue Wang
5. Carole-Jean Wu
6. Jiasi Chen
7. Zhangyang Wang

Papers from Yingyan Lin's perspective

Ordered by how strongly the researchers Yingyan Lin 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference (2019) — 93 citations
• Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference (2020) — 49 citations
• On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework (2018) — 225 citations
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (2020) — 92 citations
• Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search (2020) — 6 citations
• Drawing early-bird tickets: Towards more efficient training of deep networks (2019) — 285 citations
• EnergyNet: Energy-Efficient Dynamic Inference (2018) — 21 citations
• Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions (2018) — 121 citations
• PredictiveNet: An energy-efficient convolutional neural network via zero prediction (2017) — 61 citations