Sean Gerrish

Artificial Intelligence · 2009–2013

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 169 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #1,536 of 35,345 by trust here, #5,317 by citations
• Statistical and Nonlinear Physics — 18 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it; #141 of 1,525 by trust here, #215 by citations
• Statistics and Probability — 9 citations into it; #480 of 12,932 by trust here, #795 by citations
• Molecular Biology — 7 citations into it

Whom Sean Gerrish trusts overall

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

1. David M. Blei
2. Radford M. Neal
3. Michael I. Jordan
4. Mark Newman
5. Diederik P. Kingma
6. Matthew D. Hoffman
7. Zoubin Ghahramani
8. L. Eon Bottou
9. Tommi S. Jaakkola
10. Geoffrey E. Hinton

Who trusts Sean Gerrish overall

1. David M. Blei
2. Rajesh Ranganath
3. Dustin Tran
4. Michalis K. Titsias
5. Qiang Liu
6. Andriy Mnih
7. Diederik P. Kingma
8. Danilo Jimenez Rezende
9. Stephan Mandt
10. Matthew D. Hoffman

Papers from Sean Gerrish's perspective

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

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

Their papers

• Black Box Variational Inference (2013) — 1,251 citations
• Scalable Inference of Overlapping Communities (2012) — 118 citations
• Reading Tea Leaves: How Humans Interpret Topic Models (2009) — 2,538 citations