Pierre Boyeau

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 104 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it; #220 of 1,525 by trust here, #31 by citations
• Generative Models — 12 citations into it; #203 of 1,956 by trust here, #93 by citations
• Statistics and Probability — 6 citations into it

Whom Pierre Boyeau trusts overall

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

1. Diederik P. Kingma
2. Joseph Hilbe
3. G. Alastair Young
4. Radford M. Neal
5. Max Welling
6. Geoffrey E. Hinton
7. Yuri Burda
8. Yoshua Bengio
9. Jiarui Ding
10. Ruslan Salakhutdinov

Who trusts Pierre Boyeau overall

1. Yoshua Bengio
2. Sergey Levine
3. Yilun Du
4. Danilo Jimenez Rezende
5. Pieter Abbeel
6. Zhe Gan
7. Mohit Bansal
8. Abhinav Kumar Gupta
9. Ruslan Salakhutdinov
10. Igor Mordatch

Papers from Pierre Boyeau's perspective

Ordered by how strongly the researchers Pierre Boyeau 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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Predicting cellular responses to complex perturbations in high‐throughput screens (2023) — 316 citations
• A Python library for probabilistic analysis of single-cell omics data (2022) — 658 citations
• AUTO-ENCODING VARIATIONAL BAYES (2020)