Peter Carbonetto
Genetics · 2004–2021
A researcher in the Science corpus: 5 papers; trusts 70 researchers and is trusted by 346 researchers here, as inferred from the citation graph.
Subfields
- Genetics — 72 citations into it; #1,296 of 6,854 by trust here, #2,142 by citations
- Statistics and Probability — 57 citations into it; #5,163 of 12,932 by trust here, #7,165 by citations
- Artificial Intelligence — 35 citations into it; #8,624 of 35,345 by trust here, #13,544 by citations
- Molecular Biology — 18 citations into it
- Probabilistic & Bayesian Methods — 15 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #2,199 of 21,575 by trust here, #11,721 by citations
- Optimization & Training — 6 citations into it
- Control and Systems Engineering — 5 citations into it
Whom Peter Carbonetto trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Michael I. Jordan
- Radford M. Neal
- James O. Berger
- Tommi S. Jaakkola
- Tomohiro Ando
- Yongtao Guan
- Martin J. Wainwright
- Stefan Schaal
- Matthew Stephens
Who trusts Peter Carbonetto overall
- Jasper R. R. Uijlings
- Abhinav Kumar Gupta
- Rob Fergus
- Antonio Torralba
- Li Fei-Fei
- David M. Blei
- John Thomas Ormerod
- Li-Jia Li
- Geremy Heitz
- Anil Raj
Papers from Peter Carbonetto's perspective
Ordered by how strongly the researchers Peter Carbonetto trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A Statistical Model for General Contextual Object Recognition (2004) — 310 citations
- Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies (2012) — 292 citations
- A simple new approach to variable selection in regression, with application to genetic fine mapping (2018) — 988 citations
- Fine-mapping from summary data with the “Sum of Single Effects” model (2021) — 321 citations
- Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012) — 810 citations