Emilien Dupont

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 300 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #3,946 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 38 citations into it; #21,557 of 35,345 by trust here, #19,439 by citations
• Generative Models — 23 citations into it
• Statistical and Nonlinear Physics — 17 citations into it; #524 of 10,127 by trust here, #2,503 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Signal Processing — 8 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Emilien Dupont trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Danilo Jimenez Rezende
5. Max Welling
6. Individualized Treat
7. Alex Krizhevsky
8. Kaiming He
9. Yann LeCun
10. Ilya Sutskever

Who trusts Emilien Dupont overall

1. Fei Ye
2. Adrian G. Bors
3. Javier Antorán
4. Michael Tschannen
5. Anh Tuan Tran
6. Jason Ramapuram
7. Haozhe Feng
8. Yuxiang Wei
9. Zheng Ding
10. Peter Sorrenson

Papers from Emilien Dupont's perspective

Ordered by how strongly the researchers Emilien Dupont 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Augmented Neural ODEs (2019) — 792 citations
• Joint-VAE: Learning Disentangled Joint Continuous and Discrete Representations (2018) — 275 citations
• COIN: COmpression with Implicit Neural representations (2021) — 332 citations