Gilles Louppe

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 22 papers; trusts 731 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 401 citations into it; #3,985 of 35,345 by trust here, #347 by citations
• Probabilistic & Bayesian Methods — 221 citations into it; #351 of 1,525 by trust here, #315 by citations
• Computer Vision and Pattern Recognition — 188 citations into it; #6,637 of 21,575 by trust here, #11,498 by citations
• Statistics and Probability — 179 citations into it; #9,514 of 12,932 by trust here, #7,914 by citations
• Generative Models — 125 citations into it; #840 of 1,956 by trust here, #1,502 by citations
• Nuclear and High Energy Physics — 114 citations into it; #2,186 of 6,161 by trust here, #2,852 by citations
• Statistical and Nonlinear Physics — 71 citations into it; #1,749 of 10,127 by trust here, #2,708 by citations
• Astronomy and Astrophysics — 70 citations into it

Whom Gilles Louppe trusts overall

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

1. Leo Breiman
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Radford M. Neal
6. Jerome H. Friedman
7. Alex Krizhevsky
8. Ronald J. Williams
9. Max Welling
10. Yann LeCun

Who trusts Gilles Louppe overall

1. J. Brehmer
2. Joeri Hermans
3. Kyle Cranmer
4. Sergey I. Nikolenko
5. Cms Collaboration
6. Pablo de Castro
7. Aaron Fisher
8. George Papamakarios
9. Giuseppe Carleo
10. Volodimir Begy

Papers from Gilles Louppe's perspective

Ordered by how strongly the researchers Gilles Louppe trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Scikit-learn: Machine Learning in Python (2011) — 91,720 citations
• Learning to Pivot with Adversarial Networks (2016) — 238 citations
• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers (2015) — 273 citations
• The frontier of simulation-based inference (2019) — 1,284 citations
• Adversarial Variational Optimization of Non-Differentiable Simulators (2017) — 72 citations
• Diffusion Priors In Variational Autoencoders (2021) — 34 citations
• Unconstrained Monotonic Neural Networks (2019) — 182 citations
• Likelihood-free inference with an improved cross-entropy estimator (2018) — 53 citations
• Likelihood-free MCMC with Approximate Likelihood Ratios (2019) — 21 citations
• Recurrent machines for likelihood-free inference (2018) — 5 citations
• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018) — 33 citations
• Towards constraining warm dark matter with stellar streams through neural simulation-based inference (2020) — 32 citations
• Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019) — 270 citations
• Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019) — 80 citations
• Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019) — 63 citations
• Constraining Effective Field Theories with Machine Learning. (2018) — 172 citations
• A guide to constraining effective field theories with machine learning (2018) — 157 citations
• Mining gold from implicit models to improve likelihood-free inference (2018) — 207 citations
• QCD-aware recursive neural networks for jet physics (2017) — 201 citations
• Scikit-learn: Machine Learning Without Learning the Machinery (2015) — 507 citations