XuanLong Nguyen

Artificial Intelligence · 2007–2019

A researcher in the Science corpus: 8 papers; trusts 39 researchers and is trusted by 75 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #28,725 of 35,345 by trust here, #30,347 by citations
• Statistics and Probability — 42 citations into it; #769 of 12,932 by trust here, #974 by citations
• Statistical Learning Theory — 14 citations into it; #525 of 1,038 by trust here, #635 by citations
• Probabilistic & Bayesian Methods — 13 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Applied Mathematics — 9 citations into it
• Natural Language Processing — 8 citations into it
• Signal Processing — 8 citations into it

Whom XuanLong Nguyen trusts overall

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

1. S. Jammalamadaka
2. Amor Keziou
3. Sara A. van de Geer
4. Michael I. Jordan
5. Thomas Kailath
6. Thomas Mikosch
7. Ding-Xuan Zhou
8. Peter L. Bartlett
9. Martin J. Wainwright
10. A. van der Vaart

Who trusts XuanLong Nguyen overall

1. Qiang Liu
2. Barnab'as P'oczos
3. Fernando Perez-Cruz
4. Masashi Sugiyama
5. Jiaming Song
6. Arthur Gretton
7. Akshay Krishnamurthy
8. Sebastian Nowozin
9. Ben Poole
10. Michael Arbel

Papers from XuanLong Nguyen's perspective

Ordered by how strongly the researchers XuanLong Nguyen 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization (2007) — 154 citations
• Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization (2008) — 920 citations
• Nonparametric estimation of the likelihood ratio and divergence functionals (2007) — 40 citations
• On strong identifiability and convergence rates of parameter estimation in finite mixtures (2016) — 65 citations
• On posterior contraction of parameters and interpretability in Bayesian mixture modeling (2019) — 67 citations
• Streaming dynamic and distributed inference of latent geometric structures (2018) — 3 citations
• Scalable inference of topic evolution via models for latent geometric structures (2018) — 12 citations
• Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis (2014) — 283 citations