Antoine Wehenkel

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 185 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #18,286 of 35,345 by trust here, #26,682 by citations
• Generative Models — 36 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #11,532 of 21,575 by trust here, #17,258 by citations
• Probabilistic & Bayesian Methods — 17 citations into it; #989 of 1,525 by trust here, #1,136 by citations
• Statistical and Nonlinear Physics — 13 citations into it; #4,631 of 10,127 by trust here, #6,727 by citations
• Model Architectures — 6 citations into it; #1,319 of 2,278 by trust here, #2,128 by citations
• Signal Processing — 5 citations into it

Whom Antoine Wehenkel trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Danilo Jimenez Rezende
4. Max Welling
5. Prafulla Dhariwal
6. Yoshua Bengio
7. Aäron van den Oord
8. Alex Krizhevsky
9. Ilya Sutskever
10. Arash Vahdat

Who trusts Antoine Wehenkel overall

1. Derek Onken
2. Ivan Kobyzev
3. Ling Yang
4. Samy Wu Fung
5. Simon J. D. Prince
6. Lars Ruthotto
7. Marcus A. Brubaker
8. Zhilong Zhang
9. Arash Vahdat

Papers from Antoine Wehenkel's perspective

Ordered by how strongly the researchers Antoine Wehenkel 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Diffusion Priors In Variational Autoencoders (2021) — 34 citations
• Unconstrained Monotonic Neural Networks (2019) — 182 citations
• Recurrent machines for likelihood-free inference (2018) — 5 citations