Théo Galy-Fajou

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #30,631 of 35,345 by trust here, #30,347 by citations
• Probabilistic & Bayesian Methods — 17 citations into it
• Statistics and Probability — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Théo Galy-Fajou trusts overall

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

1. James Hensman
2. Michalis K. Titsias
3. David M. Blei
4. Alexander G. de G. Matthews
5. Matthew D. Hoffman
6. Christopher K. I. Williams
7. Diederik P. Kingma
8. David Arthur
9. Zoubin Ghahramani
10. Radford M. Neal

Who trusts Théo Galy-Fajou overall

1. Jake Snell
2. Idan Achituve
3. Richard S. Zemel
4. Aviv Navon
5. Haitao Liu

Papers from Théo Galy-Fajou's perspective

Ordered by how strongly the researchers Théo Galy-Fajou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation (2019) — 32 citations
• Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation (2018) — 40 citations