Francesco Cagnetta

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it
• Scaling & Learning Theory — 10 citations into it
• Model Architectures — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Statistical Learning Theory — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Francesco Cagnetta trusts overall

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

1. Julien Mairal
2. Christopher K. I. Williams
3. Blake Bordelon
4. Abdulkadir Canatar
5. Youngmin Cho
6. Alberto Bietti
7. Yann LeCun
8. Arthur Jacot
9. Lénaïc Chizat
10. Francis R. Bach

Papers from Francesco Cagnetta's perspective

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

Their papers

• Locality defeats the curse of dimensionality in convolutional teacher–student scenarios (2021) — 33 citations