Quentin de Laroussilhe

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 152 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #8,364 of 35,345 by trust here, #3,072 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #7,494 of 21,575 by trust here, #2,381 by citations
• Model Architectures — 19 citations into it
• Optimization & Training — 15 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Neural Architecture Search & AutoML — 9 citations into it; #175 of 283 by trust here, #260 by citations

Whom Quentin de Laroussilhe trusts overall

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

1. Quoc V. Le
2. Barret Zoph
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Diederik P. Kingma
8. Karen Simonyan
9. Ilya Sutskever
10. Yann LeCun

Who trusts Quentin de Laroussilhe overall

1. Jonas Pfeiffer
2. Colin White
3. Zixuan Ke
4. Renrui Zhang
5. Yi-Lin Sung
6. Ivan Vulic
7. Andreas Ruckl'e
8. B. Ermiş

Papers from Quentin de Laroussilhe's perspective

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

Their papers

• Parameter-Efficient Transfer Learning for NLP (2019) — 6,877 citations
• Evolutionary-Neural Hybrid Agents for Architecture Search (2018) — 44 citations