Linda S. L. Tan

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 84 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #10,248 of 35,345 by trust here, #23,077 by citations
• Statistics and Probability — 47 citations into it; #3,584 of 12,932 by trust here, #6,310 by citations
• Probabilistic & Bayesian Methods — 31 citations into it
• Optimization & Training — 16 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #19,541 of 21,575 by trust here, #21,103 by citations
• Management Science and Operations Research — 6 citations into it
• Environmental Engineering — 5 citations into it

Whom Linda S. L. Tan trusts overall

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

1. Radford M. Neal
2. David M. Blei
3. Michael I. Jordan
4. Matthew D. Hoffman
5. Matt P. Wand
6. David J. Nott
7. Zoubin Ghahramani
8. Diederik P. Kingma
9. John William Paisley
10. David A. Knowles

Who trusts Linda S. L. Tan overall

1. David J. Nott
2. David Rohde
3. B. Bugbee
4. Geoffrey Roeder
5. Matt P. Wand
6. Ming Xu
7. F. J. Breidt
8. Yuhuai Wu
9. Matias Quiroz
10. David M. Blei

Papers from Linda S. L. Tan's perspective

Ordered by how strongly the researchers Linda S. L. Tan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Gaussian variational approximation with sparse precision matrices (2016) — 89 citations
• Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations (2012) — 49 citations
• A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models (2012) — 26 citations