A. Berger

Probabilistic & Bayesian Methods · with DeWeese · 2015

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 10 citations into it
• Statistics and Probability — 10 citations into it
• Artificial Intelligence — 7 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom A. Berger trusts overall

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

1. Radford M. Neal
2. Jascha Narain Sohl-Dickstein
3. Mark A. Girolami
4. Jesus A. Izaguirre
5. David F. Anderson
6. Jasper Snoek
7. Ziyun Wang
8. Tianqi Chen
9. Edward Meeds
10. Alexandros Beskos

Who trusts A. Berger overall

1. Daniel Lévy
2. Matthew D. Hoffman

Papers from A. Berger's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015)