Evan Munro

Statistics and Probability · 2019

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 27 citations into it
• Artificial Intelligence — 13 citations into it
• Causal Inference & Discovery — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Generative Models — 8 citations into it

Whom Evan Munro trusts overall

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

1. Max H. Farrell
2. Stefan Wager
3. Susan Athey

Papers from Evan Munro's perspective

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

1. Dropout Training as Adaptive Regularization
2. Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
3. Approximate residual balancing: debiased inference of average treatment effects in high dimensions
4. Towards Principled Methods for Training Generative Adversarial Networks
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations (2019) — 105 citations