Guillaume Lajoie

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it
• Scaling & Learning Theory — 53 citations into it
• Optimization & Training — 35 citations into it
• Statistical Learning Theory — 22 citations into it
• Reinforcement Learning — 17 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Guillaume Lajoie trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Richard S. Sutton
7. Yann LeCun
8. Karen Simonyan
9. Peter L. Bartlett
10. Ilya Sutskever

Papers from Guillaume Lajoie's perspective

Ordered by how strongly the researchers Guillaume Lajoie 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. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Gradient Starvation: A Learning Proclivity in Neural Networks (2020) — 353 citations
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning (2021) — 77 citations
• Implicit Regularization via Neural Feature Alignment (2020) — 71 citations