Laura Douglas

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Laura Douglas trusts overall

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

1. Radford M. Neal
2. Quaid D. Morris
3. Diederik P. Kingma
4. Samuel J. Gershman
5. Brooks Paige
6. Shixiang Shane Gu
7. William W. Cohen
8. Geoffrey E. Hinton
9. Jimmy Ba
10. Mathieu Germain

Who trusts Laura Douglas overall

1. Ian Covert
2. Oleg Ivanov
3. Yura N. Perov
4. Nan Rosemary Ke
5. Javier Antorán Cabiscol

Papers from Laura Douglas's perspective

Ordered by how strongly the researchers Laura Douglas 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Dropout: a simple way to prevent neural networks from overfitting
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Universal Marginalizer for Amortized Inference in Generative Models (2017) — 31 citations