Luca Franceschi

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 79 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #9,040 of 35,345 by trust here, #9,641 by citations
• Optimization & Training — 40 citations into it; #1,607 of 2,601 by trust here, #1,571 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #12,379 of 21,575 by trust here, #9,305 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Graph Learning — 16 citations into it
• Model Architectures — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Luca Franceschi trusts overall

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

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

Who trusts Luca Franceschi overall

1. Jonathan Lorraine
2. Kaiyi Ji
3. Yunhun Jang
4. Zhongwen Xu
5. Quanming Yao
6. Paul Vicol
7. Timothy M. Hospedales
8. Chao Shang
9. Yaoyao Liu
10. Edward Grefenstette

Papers from Luca Franceschi's perspective

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

Their papers

• Bilevel Programming for Hyperparameter Optimization and Meta-Learning (2018) — 888 citations
• Forward and Reverse Gradient-Based Hyperparameter Optimization (2017) — 477 citations
• Learning Discrete Structures for Graph Neural Networks (2019) — 522 citations