David G. Andersen

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #10,748 of 35,345 by trust here, #11,921 by citations
• Safety, Robustness & Interpretability — 67 citations into it; #809 of 2,251 by trust here, #1,047 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #8,705 of 21,575 by trust here, #14,478 by citations
• Signal Processing — 20 citations into it; #2,177 of 7,572 by trust here, #1,629 by citations
• Generative Models — 6 citations into it

Whom David G. Andersen trusts overall

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

1. Ronald J. Williams
2. Ian J. Goodfellow
3. Jakob N. Foerster
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Diederik P. Kingma
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ilya Sutskever
10. Shafi Goldwasser

Who trusts David G. Andersen overall

1. Chong Huang
2. Jamie Hayes
3. Peter Kairouz
4. Jiachun Liao
5. Sanjeev Arora
6. Jie Gui
7. Ryan J. Lowe
8. George Danezis

Papers from David G. Andersen's perspective

Ordered by how strongly the researchers David G. Andersen 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Motivating the Rules of the Game for Adversarial Example Research (2018) — 240 citations
• Learning to Protect Communications with Adversarial Neural Cryptography (2016) — 235 citations