Jason Stanley

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Probabilistic & Bayesian Methods — 23 citations into it

Whom Jason Stanley trusts overall

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

1. Radford M. Neal

Papers from Jason Stanley's perspective

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

1. Pattern Recognition and Machine Learning
2. Bayesian learning for neural networks
3. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
4. Bayesian Learning via Stochastic Gradient Langevin Dynamics
5. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
6. Information Theory, Inference, and Learning Algorithms
7. A Practical Bayesian Framework for Backpropagation Networks
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Variational Dropout and the Local Reparameterization Trick
10. ImageNet classification with deep convolutional neural networks

Their papers

• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020) — 336 citations