Jean Pouget-Abadie

Computer Vision and Pattern Recognition · 2014–2025

A researcher in the Science corpus: 6 papers; trusts 109 researchers and is trusted by 316 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 201 citations into it; #2,312 of 21,575 by trust here, #792 by citations
• Artificial Intelligence — 176 citations into it; #34,354 of 35,345 by trust here, #13,353 by citations
• Generative Models — 135 citations into it; #428 of 1,956 by trust here, #48 by citations
• Optimization & Training — 43 citations into it
• Probabilistic & Bayesian Methods — 32 citations into it
• Natural Language Processing — 31 citations into it
• Model Architectures — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it

Whom Jean Pouget-Abadie trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Ilya Sutskever
5. Alex Graves
6. Diederik P. Kingma
7. Yann LeCun
8. Kaiming He
9. Kyunghyun Cho
10. Ruslan Salakhutdinov

Who trusts Jean Pouget-Abadie overall

1. Fei Ye
2. Richard Zhang
3. Yoshua Bengio
4. Jun-Yan Zhu
5. Luc Van Gool
6. Chunyuan Li
7. Alan L. Yuille
8. Jaemin Cho
9. Timothée Lesort
10. Trevor Darrell

Papers from Jean Pouget-Abadie's perspective

Ordered by how strongly the researchers Jean Pouget-Abadie 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generative Adversarial Networks (2021) — 30,470 citations
• Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation (2014) — 84 citations
• Generative adversarial networks (2014) — 4,455 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Gemma 3 Technical Report (2025) — 1,621 citations
• Generative Adversarial Nets (2014) — 2,369 citations