Vincent Fortuin

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #30,867 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #19,025 of 21,575 by trust here, #13,815 by citations
• Generative Models — 17 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Signal Processing — 11 citations into it
• Optimization & Training — 10 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Vincent Fortuin trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Yann LeCun
4. Geoffrey E. Hinton
5. Teuvo Kohonen
6. Michael I. Jordan
7. L. Eon Bottou
8. Jimmy Ba
9. Alex Krizhevsky
10. Neil D. Lawrence

Who trusts Vincent Fortuin overall

1. Juan Miguel Lopez Alcaraz
2. Francesco Locatello
3. Pablo Moreno-Muñoz
4. Nils Strodthoff
5. Michael Tschannen
6. Antonio Artés-Rodríguez
7. Olivier Bachem

Papers from Vincent Fortuin's perspective

Ordered by how strongly the researchers Vincent Fortuin 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019) — 155 citations
• GP-VAE: Deep Probabilistic Time Series Imputation (2019) — 402 citations
• Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018) — 16 citations