Baptiste Goujaud
Artificial Intelligence · 2019
A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 67 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 27 citations into it; #4,132 of 35,345 by trust here, #4,722 by citations
- Computer Vision and Pattern Recognition — 22 citations into it; #3,857 of 21,575 by trust here, #3,582 by citations
- Reinforcement Learning — 14 citations into it
- Meta & Continual Learning — 12 citations into it; #65 of 1,041 by trust here, #80 by citations
- Representation & Self-Supervised Learning — 10 citations into it
Whom Baptiste Goujaud trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Anthony V. Robins
- Rahaf Aljundi
- Sylvestre-Alvise Rebuffi
- Geoffrey E. Hinton
- Alex Krizhevsky
- Marcus Rohrbach
- Sebastian Farquhar
- Yoshua Bengio
- Friedemann Zenke
- Diederik P. Kingma
Who trusts Baptiste Goujaud overall
- Lucas Caccia
- Jiangpeng He
- Bo Zhao
- Xisen Jin
- Rahaf Aljundi
- Zheda Mai
- Matteo Boschini
- Fei Ye
- Yiduo Guo
- Pietro Buzzega
Papers from Baptiste Goujaud's perspective
Ordered by how strongly the researchers Baptiste Goujaud trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Gradient based sample selection for online continual learning (2019) — 1,049 citations
- Online continual learning with no task boundaries (2019) — 45 citations