Philippe Brouillard

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #24,979 of 35,345 by trust here, #20,836 by citations
• Causal Inference & Discovery — 42 citations into it; #280 of 364 by trust here, #212 by citations
• Statistics and Probability — 23 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Graph Learning — 8 citations into it
• Optimization & Training — 7 citations into it
• Generative Models — 6 citations into it

Whom Philippe Brouillard trusts overall

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

1. Jan Peters
2. Peter Buhlmann
3. Tom L. Burr
4. Danilo Jimenez Rezende
5. Yoshua Bengio
6. Bernhard Schölkopf
7. Kun Zhang
8. David Maxwell Chickering
9. Geoffrey E. Hinton
10. Arthur Gretton

Who trusts Philippe Brouillard overall

1. Sébastien Lachapelle
2. Gonçalo Faria
3. Rémi Le Priol
4. Chao Shang
5. Ignavier Ng
6. Xun Zheng

Papers from Philippe Brouillard's perspective

Ordered by how strongly the researchers Philippe Brouillard 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Gradient-Based Neural DAG Learning (2019) — 359 citations
• Differentiable Causal Discovery from Interventional Data (2020) — 282 citations