Francesca Babiloni

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 316 researchers and is trusted by 167 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 61 citations into it; #2,842 of 21,575 by trust here, #1,669 by citations
• Artificial Intelligence — 60 citations into it; #3,481 of 35,345 by trust here, #2,263 by citations
• Representation & Self-Supervised Learning — 32 citations into it; #589 of 3,145 by trust here, #275 by citations
• Meta & Continual Learning — 19 citations into it; #860 of 1,041 by trust here, #940 by citations
• Optimization & Training — 15 citations into it
• Model Architectures — 12 citations into it
• Media Technology — 9 citations into it
• Reinforcement Learning — 7 citations into it

Whom Francesca Babiloni trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Andrew Zisserman
5. Ilya Sutskever
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Diederik P. Kingma
10. Tomas Mikolov

Who trusts Francesca Babiloni overall

1. Rahaf Aljundi
2. Fan-Keng Sun
3. Marc Masana
4. Tyler L. Hayes
5. Tinne Tuytelaars
6. Zheda Mai
7. Da-Wei Zhou
8. Mohammad Rostami
9. Xialei Liu
10. Liyuan Wang

Papers from Francesca Babiloni's perspective

Ordered by how strongly the researchers Francesca Babiloni 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Memory Aware Synapses: Learning what (not) to forget (2017) — 2,096 citations
• Memory Aware Synapses: Learning what (not) to forget (2018)
• Exploring the Challenges towards Lifelong Fact Learning (2018) — 12 citations