Jon D. McAuliffe
Artificial Intelligence · 2003–2018
A researcher in the Science corpus: 9 papers; trusts 192 researchers and is trusted by 421 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 129 citations into it; #864 of 35,345 by trust here, #1,822 by citations
- Statistics and Probability — 104 citations into it; #800 of 12,932 by trust here, #578 by citations
- Probabilistic & Bayesian Methods — 58 citations into it; #255 of 1,525 by trust here, #200 by citations
- Management Science and Operations Research — 30 citations into it
- Statistical Learning Theory — 29 citations into it; #192 of 1,038 by trust here, #130 by citations
- Applied Mathematics — 24 citations into it; #5,605 of 7,620 by trust here, #4,482 by citations
- Computer Vision and Pattern Recognition — 22 citations into it; #3,542 of 21,575 by trust here, #3,692 by citations
- Optimization & Training — 20 citations into it
Whom Jon D. McAuliffe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael I. Jordan
- David M. Blei
- Joel A. Tropp
- Radford M. Neal
- Donald B. Rubin
- Eric R. Ziegel
- Geoffrey E. Hinton
- Robert Tibshirani
- Diederik P. Kingma
- Shun-ichi Amari
Who trusts Jon D. McAuliffe overall
- David M. Blei
- Francis R. Bach
- Ruslan Salakhutdinov
- Chong Wang
- Julien Mairal
- Simon Lacoste-Julien
- Jun Zhu
- Tong Zhang
- Yoshua Bengio
- Michael I. Jordan
Papers from Jon D. McAuliffe's perspective
Ordered by how strongly the researchers Jon D. McAuliffe 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Variational Inference: A Review for Statisticians (2016) — 5,822 citations
- Supervised Topic Models (2007) — 1,826 citations
- Convexity, Classification, and Risk Bounds (2006) — 1,578 citations
- Variational Inference for Large-Scale Models of Discrete Choice (2007) — 193 citations
- Time-uniform, nonparametric, nonasymptotic confidence sequences (2018) — 383 citations
- Time-uniform Chernoff bounds via nonnegative supermartingales (2018) — 220 citations
- Uniform, nonparametric, non-asymptotic confidence sequences (2018) — 53 citations
- Celeste: Variational inference for a generative model of astronomical images (2015) — 40 citations
- Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates (2003) — 57 citations