Darryl Dexu Lin

Artificial Intelligence · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #33,371 of 35,345 by trust here, #31,361 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #6,526 of 21,575 by trust here, #7,852 by citations
• Efficiency & Compression — 13 citations into it; #341 of 1,264 by trust here, #613 by citations
• Model Architectures — 7 citations into it; #743 of 2,278 by trust here, #1,092 by citations

Whom Darryl Dexu Lin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Kaiming He
4. Alex Krizhevsky
5. Matthieu Courbariaux
6. Sergey Ioffe
7. Christian Szegedy
8. Song Han
9. Yann LeCun
10. Ilya Sutskever

Who trusts Darryl Dexu Lin overall

1. Georgios Georgiadis
2. Jeff Johnson
3. Raj Dabre
4. Zhaowei Cai
5. Yury Nahshan
6. Yunhui Guo
7. Ruizhou Ding
8. Itay Hubara
9. Yelysei Bondarenko
10. Simon Wiedemann

Papers from Darryl Dexu Lin's perspective

Ordered by how strongly the researchers Darryl Dexu 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Fixed Point Quantization of Deep Convolutional Networks (2015) — 864 citations
• Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks (2016) — 48 citations