Federico Monti

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 228 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #5,109 of 35,345 by trust here, #3,059 by citations
• Graph Learning — 146 citations into it; #139 of 693 by trust here, #87 by citations
• Computer Vision and Pattern Recognition — 109 citations into it; #7,535 of 21,575 by trust here, #2,915 by citations
• Computational Mechanics — 59 citations into it; #2,698 of 10,001 by trust here, #417 by citations
• Statistical and Nonlinear Physics — 58 citations into it; #3,140 of 10,127 by trust here, #1,047 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Molecular Biology — 36 citations into it
• Information Systems — 33 citations into it

Whom Federico Monti trusts overall

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

1. Yann LeCun
2. Michael M. Bronstein
3. Diederik P. Kingma
4. Joan Bruna
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Davide Boscaini
8. Xavier Bresson
9. Alex Krizhevsky
10. Thomas Kipf

Who trusts Federico Monti overall

1. Michael M. Bronstein
2. Xavier Bresson
3. Aleksandar Bojchevski
4. Vijay Prakash Dwivedi
5. Matthias Fey
6. Or Litany
7. Yue Wang
8. Stephan Günnemann
9. Sarah Parisot
10. Thomas Laurent

Papers from Federico Monti's perspective

Ordered by how strongly the researchers Federico Monti 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016) — 1,972 citations
• SIGN: Scalable Inception Graph Neural Networks (2020) — 475 citations
• CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters (2017) — 754 citations
• Fake News Detection on Social Media using Geometric Deep Learning (2019) — 566 citations
• Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020) — 1,099 citations
• Dual-Primal Graph Convolutional Networks (2018) — 84 citations
• MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS (2018) — 153 citations
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017) — 554 citations