Nicolas Sonnerat

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 74 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 154 citations into it; #31,951 of 35,345 by trust here, #7,531 by citations
• Reinforcement Learning — 122 citations into it; #1,393 of 2,242 by trust here, #433 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Management Science and Operations Research — 32 citations into it
• Generative Models — 25 citations into it
• Optimization & Training — 20 citations into it

Whom Nicolas Sonnerat trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Alex Krizhevsky
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Yann LeCun
10. Jimmy Ba

Who trusts Nicolas Sonnerat overall

1. Francesco Locatello
2. Drew A. Hudson
3. Jason Jo

Papers from Nicolas Sonnerat's perspective

Ordered by how strongly the researchers Nicolas Sonnerat 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Reinforcement Learning and the Deadly Triad (2018) — 287 citations
• SCAN: Learning Abstract Hierarchical Compositional Visual Concepts (2017) — 53 citations
• SCAN: Learning Hierarchical Compositional Visual Concepts (2017) — 134 citations
• Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 (2024) — 377 citations
• Solving Mixed Integer Programs Using Neural Networks (2020) — 322 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward (2018) — 895 citations
• Human-level performance in 3D multiplayer games with population-based reinforcement learning (2018) — 819 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning (2017) — 1,350 citations