Federico Errica

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 75 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #30,480 of 35,345 by trust here, #19,873 by citations
• Graph Learning — 65 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it; #18,786 of 21,575 by trust here, #13,269 by citations
• Molecular Biology — 26 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Materials Chemistry — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Federico Errica trusts overall

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

1. Thomas Kipf
2. Diederik P. Kingma
3. Max Welling
4. Alessio Micheli
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Muhan Zhang
8. Martin Simonovsky
9. William L. Hamilton
10. Yann LeCun

Who trusts Federico Errica overall

1. Uri Alon
2. Vijay Prakash Dwivedi
3. Deyu Bo
4. Christopher Morris
5. Zhanghao Wu
6. Zonghan Wu
7. Julien N. Siems
8. Eran Yahav
9. Yassine Ouali
10. Chaitanya K. Joshi

Papers from Federico Errica's perspective

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

Their papers

• A Fair Comparison of Graph Neural Networks for Graph Classification (2019) — 527 citations
• A Gentle Introduction to Deep Learning for Graphs (2019) — 326 citations