Rémy Sun

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 141 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it; #13,558 of 21,575 by trust here, #14,822 by citations
• Generative Models — 30 citations into it
• Causal Inference & Discovery — 27 citations into it; #238 of 364 by trust here, #242 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Model Architectures — 8 citations into it
• Safety, Robustness & Interpretability — 6 citations into it; #1,500 of 2,251 by trust here, #1,907 by citations

Whom Rémy Sun trusts overall

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

1. Dominik Janzing
2. Bernhard Schölkopf
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Jan Peters
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Danilo Jimenez Rezende
9. Michel Besserve
10. Yann LeCun

Who trusts Rémy Sun overall

1. Bernhard Schölkopf
2. Michel Besserve
3. Felix Leeb
4. Stefan Bauer

Papers from Rémy Sun's perspective

Ordered by how strongly the researchers Rémy Sun 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Counterfactuals uncover the modular structure of deep generative models (2018) — 110 citations
• A theory of independent mechanisms for extrapolation in generative models (2020) — 28 citations
• Intrinsic disentanglement: an invariance view for deep generative models (2018) — 4 citations