Lin Lin

Artificial Intelligence · with Liu · 2016

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Safety, Robustness & Interpretability — 22 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Lin Lin trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Ian J. Goodfellow
4. Nicolas Papernot
5. Otmar Scherzer
6. Yoshua Bengio
7. Alex Krizhevsky
8. Sam T. Roweis
9. David E. Rumelhart
10. Christian Szegedy

Papers from Lin Lin's perspective

Ordered by how strongly the researchers Lin 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Dropout: a simple way to prevent neural networks from overfitting
9. ImageNet: A large-scale hierarchical image database
10. Intriguing properties of neural networks

Their papers

• Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks (2016) — 10 citations
• Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks (2016) — 10 citations