François‐Xavier Briol

Statistics and Probability · 2015–2021

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 51 citations into it; #11,408 of 12,932 by trust here, #12,077 by citations
• Artificial Intelligence — 43 citations into it; #20,300 of 35,345 by trust here, #26,682 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistical Learning Theory — 13 citations into it
• Statistical and Nonlinear Physics — 12 citations into it; #6,543 of 10,127 by trust here, #7,402 by citations
• Numerical Analysis — 9 citations into it
• Optimization & Training — 8 citations into it
• Numerical Methods & Scientific Computing — 6 citations into it

Whom François‐Xavier Briol trusts overall

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

1. Carl E. Rasmussen
2. Anthony OʼHagan
3. Chris J. Oates
4. Francis R. Bach
5. Simon Lacoste-Julien
6. Philipp Hennig
7. Mark A. Girolami
8. Ali Rahimi
9. Christopher K. I. Williams
10. Ferenc Huszár

Who trusts François‐Xavier Briol overall

1. Qiang Liu
2. Rémi Bardenet
3. Motonobu Kanagawa
4. Jason D. Lee
5. Adrien Hardy
6. Philipp Hennig
7. Jackson Gorham

Papers from François‐Xavier Briol's perspective

Ordered by how strongly the researchers François‐Xavier Briol 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. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
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

• Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees (2015) — 97 citations
• Convergence rates for a class of estimators based on Stein’s method (2016) — 61 citations
• Robust generalised Bayesian inference for intractable likelihoods (2021) — 109 citations