Nicoló Navarin

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 7 papers; trusts 52 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #12,907 of 35,345 by trust here, #19,873 by citations
• Graph Learning — 56 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it; #9,454 of 21,575 by trust here, #14,822 by citations
• Computational Theory and Mathematics — 18 citations into it
• Molecular Biology — 17 citations into it
• Statistical Learning Theory — 14 citations into it
• Computational Chemistry & Drug Discovery — 11 citations into it
• Natural Language Processing — 10 citations into it

Whom Nicoló Navarin trusts overall

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

1. Fabrizio Costa
2. Nino Shervashidze
3. Karsten M. Borgwardt
4. Pierre Mahé
5. Geoffrey E. Hinton
6. Giovanni Da San Martino
7. S. V. N. Vishwanathan
8. Martin Simonovsky
9. Marion Neumann
10. Nikil Wale

Who trusts Nicoló Navarin overall

1. Giovanni Da San Martino
2. Nils M. Kriege
3. Alessandro Sperduti
4. Anton Tsitsulin
5. Davide Bacciu
6. Fredrik D. Johansson
7. Dinh Van Tran
8. John Palowitch
9. Weihua Hu
10. Christopher Morris

Papers from Nicoló Navarin's perspective

Ordered by how strongly the researchers Nicoló Navarin 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. 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. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Pre-training Graph Neural Networks with Kernels (2018) — 30 citations
• A memory efficient graph kernel (2012) — 16 citations
• Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’ (2020) — 897 citations
• On Filter Size in Graph Convolutional Networks (2018) — 53 citations
• Tree-Based Kernel for Graphs With Continuous Attributes (2015) — 28 citations
• Ordered Decompositional DAG kernels enhancements (2015) — 22 citations
• A Tree-Based Kernel for Graphs (2012) — 56 citations