John Lawson

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 18 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #18,279 of 35,345 by trust here, #15,236 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #11,545 of 21,575 by trust here, #10,334 by citations
• Generative Models — 6 citations into it

Whom John Lawson trusts overall

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

1. Diederik P. Kingma
2. Andriy Mnih
3. Ronald J. Williams
4. Ruslan Salakhutdinov
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Ilya Sutskever
8. Peter W. Glynn
9. Radford M. Neal
10. Danilo Jimenez Rezende

Who trusts John Lawson overall

1. Arash Vahdat
2. Avishek Joey Bose

Papers from John Lawson's perspective

Ordered by how strongly the researchers John Lawson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models (2017) — 303 citations